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EFFICACY OF SOLITARY AND CONJOINT GUIDED IMAGERY WITH
BREAST CANCER PATIENTS

By

Ellen Leroi

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Family and Child Ecology

1997

ABSTRACT

EFFICACY OF SOLITARY AND CONJOINT GUIDED IMAGERY WITH
BREAST CANCER PATIENTS

By

Ellen Leroi

The purpose of this research was to study the effects of imagery and
active spousal support on blood counts in women with diagnosed
premenopausal breast cancer who had followed a standard course of treat-
ment. For each participating couple a 15-day protocol was followed. In the
initial session a baseline blood draw preceded a fifteen-minute immune
system videotaped presentation (Bioimagery, 1993). In the two succeeding
weeks pre-and post-treatment blood samples were obtained. The subjects
utilized guided immunoimagery, alone and with their spouse, to ascertain
whether imaging with a partner affects the desired increments in the assayed
numerosities following imagery. For thirty minutes after each imagery
session, prior to venipuncture, the woman or couple was asked to draw the
cancer and their corresponding immunoimages. The pictorial accounts were
assessed relative to the patient’s and spouse’s attitudes about the virulence
of the cancer, and the power of the patient’s immune system. The drawings
were rated according to the IMAGE-CA developed by Achterberg and Lawlis
(1984); the relative strength and vividness of the depicted cancer cells and

immune system cells were related to blood count changes for white blood cells,

Ellen Leroi
absolute lymphocytes, total T-cells, helper/inducer T-cells, suppressor/cyto-
toxic T-cells, and segmented neutrophils. The couples’ satisfaction and
adjustment within the marital relationship (DAS scores/Spanier, 1976) was
an intervening variable for both the solitary and conjoint imagery and the
subsequent blood assays.

In only 8 of 72 instances did blood counts increase by more than 10%
over the course of an imagery session; in slightly more than half the cases
blood counts decreased by more than 10%. There was no significant two-week
learning effect nor was there statistical support for the notion that conjoint
imagery has an incrementing effect on blood count numerosity over a two-
week time span. For all of the blood measures except segmented neutrophils,
the probability that blood counts will stay the same or increase following the
guided imagery was inversely related to the IMAGE-CA Score. There was no
relationship between marital satisfaction and adjustment and changes in
blood counts.

The uniform decreases in lymphocyte counts following imagery
measured in this investigation (although generally within measurement
error) are consistent with results in the few published studies evaluating
short term blood cell changes following behavioral treatment. Clearly, a

longer term study is indicated.

Achterberg, J. and G. F. Lawlis (1984). W. Champaign, IL:
Institute for Personality and Ability Testing, Inc.

Bioimagery (1993). The science of immuno—imagery. Irvine, CA: Bioimagery.

Spanier, G. B. (1976). “Measuring dyadic adjustment: New scales for
assessing the quality of marriage and similar dyads.” Journal of
Marriage and the Family, 38: 15-28.

ACKNOWLEDGMENTS

I am especially grateful to George Leroi, my husband, chief supporter
and dearest friend. His love and encouragement are ever present. I wish to
thank my children, John and Ben and my step-children, David and Chris for
their kindness, patience and collective senses of humor. My sister-in-law and
brother-in-law, Rony and Dick Soulen offered continual support throughout
this project. I appreciate my mother’s conviction and determination to
educate all of her children. I am thankful to my brother, David, for his
lifelong caring and loving. My family is my foundation and I thank them all.

Iparticularly appreciate my Guidance Committee: Dr. Donald Melcer,
Chairman, Dr. Dolores Borland-Hunt, Dr. John Schneider and Dr. Anne
Soderman, whose synergetic enthusiasm has been felt throughout graduate
study. I have been truly mentored by each and I am grateful.

I wish to thank Dr. G. Marie Swanson, Dr. Mary Zabik, Dr. Marjorie
Kostelnik, and Dr. Percy Pierre for partially funding this research, and for
their generous offers to lend their academic expertise to the project. Professor
Donald Beaver introduced me the potentiality of logistic regression; he was
particularly patient and encouraging. On many occasions Dr. P. K Wong
generously and graciously provided invaluable technical support.

Dorothy Mirkil, my fellow student and friend, deserves a special note of
thanks for volunteering many weekend mornings to complete the blood draws
for this investigation. Art and Cindy Meyers generously provided lovely and
comfortable space for this project. Profound gratitude is extended to each

iv

couple who chose to participate in this study -- each willingly volunteered
considerable time and energy in the hope that her/his participation would
favorably impact future cancer patients and their families.

To each professor and each of my fellow graduate students: I have
learned from you, enjoyed our interactions, and am indebted for the enriching
experiences we shared together. I wish to honor Mary Jo Stibitz, who was a
fellow student and loyal friend; thoughts of her accompany me as I graduate.

TABLE OF CONTENTS

LIST OF TABLES ...............................................................................................
LIST OF FIGURES ..............................................................................................
LIST OF DRAWINGS, APPENDIX J ..............................................................

CHAPTER I. PROLOGUE .............................................................................

Introduction .............................................................................................
Background ..............................................................................................
Problem Statement ................................................................................

CHAPTER II. REVIEW OF THE LITERATURE .....................................

CHAPTER HI. RESEARCH METHODOLOGY ..........................................

Conceptual Model ...................................................................................
Research Objectives ...............................................................................
Conceptual and Operational Definitions ..........................................
Research Assumptions and Conditions ............................................
Research Questions ................................................................................
Research Hypotheses ............................................................................
Research Design .....................................................................................
Instrumentation and Measurement Procedures .............................
Sampling Methods and Data Collection Procedures ......................
Data Analysis .........................................................................................
Limitations of the Research ................................................................

CHAPTER IV. RESULTS AND DISCUSSION .........................................

Demographic Information ....................................................................
Laboratory Results and Primary Data .............................................
Interpretation .........................................................................................
Statistical Analysis ...............................................................................

vi

Page

viii

36
36
42
44
44
45
46
48
51
55
58
60

61
61
63
63
87

CHAPTER V. SUMNIARY AND EPILOGUE ............................................ 96

Summary .................................................................................................. 96
Recommendations for Future Work ................................................... 98
APPENDICES
A. Dyadic Adjustment Scale ............................................................... 102
B. Laboratory Blood Test Procedures ............................................... 104
C. Immunoimagery Videotape Script ............................................... 1 11
D. Immunoimagery Audiotape Script ............................................... 115
E. IMAGE-CA ........................................................................................ 120
F. Participants’ Informed Consent Agreements ............................ 125
G. Solicitation Letter to Potential Subjects ................................... 127
H. UCRIHS ............................................................................................. 129
1. Medical Release and Information Forms ................................... 131
J. Wives’ IMAGE- CA Drawings ......................................................... 140
K LogXact Output ................................................................................ 158
BIBLIOGRAPHY .................................................................................................. 194

vii

LIST OF TABLES

Table Page

1. Research Protocol ............................................................................................... 52

2. White blood cell counts (cells/uL) relative to sequence and to
solitary vs. conjoint imagery ............................................................................ 64

3. Absolute lymphocyte counts (cells/uL) relative to sequence and
to solitary vs. conjoint imagery ...................................................................... 65

4. CD3 counts (cells/uL) relative to sequence and to solitary vs.
conjoint imagery ................................................................................................. 66

5. CD4 counts (cells/uL) relative to sequence and to solitary vs.
conjoint imagery ................................................................................................. 67

6. CD8 counts (cells/uL) relative to sequence and to solitary vs.
conjoint imagery ................................................................................................. 68

7. Segmented neutrophil counts (cells/uL) relative to sequence and

to solitary vs. conjoint imagery ....................................................................... 69
8. IMAGE-CA scores .............................................................................................. 7O
9. Dyadic Adjustment Scale scores ..................................................................... 71
10. Interrater average IMAGE-CA scores for the wives ................................... 7 5

11. Post-imagery blood draw numerosities compared to the corres-
ponding pre-imagery values, sorted by session sequence .......................... 88

12. Post-imagery blood draw numerosities compared to the corres-

ponding pre-imagery values, sorted according to whether the
wife imaged alone or together with her husband ........................................ 89

viii

13. Exact hypothesis test results (conditional exact inference) for two
models for changes in blood assay numerosities during imagery
sessions, obtained with LogXact software. (The null hypothesis
could not be rejected for the coefficients of Alone/Together or
Sequence.) ............................................................................................................ 94

LIST OF FIGURES

Figure Page
1. Schematic representation of the role of the limbic-hypothalamic

system on the autonomic, endocrine and immune systems .................. 6
2. Major communication paths between the mind-brain and the

interacting networks of the immune system ............................................ 8
3. Hierarchy of Natural Systems ..................................................................... 39
4. Continuum of Natural Systems .................................................................. 4O
5. Trend Possibilities ......................................................................................... 72
6. Illustration of trends individual data being obscured by averaging 73
7. Example of how opposing trends are obscured by averaging ................. 74
8. IMAGE-CA and blood count changes for Couple 1 .................................. 76
9. IMAGE-CA and blood count changes for Couple 2 .................................. 7 7
10. IMAGE-CA and blood count changes for Couple 3 .................................. 78
11. IMAGE-CA and blood count changes for Couple 4 .................................. 79
12. IMAGE-CA and blood count changes for Couple 5 .................................. 80
13. IMAGE-CA and blood count changes for Couple 6 .................................. 81

LIST OF DRAWINGS, APPENDD( J

Drawing Page
1. Subject 1, image alone, 12-10-94 ................................................................ 140
2. Subject 1, image together, 12-17-94 ........................................................... 141
3. Subject 2, image alone, 12-18-94 ................................................................ 142
4. Subject 2, image together, 12-11-94 ........................................................... 143
5. Subject 3, image alone, 2-5-95 ..................................................................... 144
6. Subject 3, image together, 2-12-95 ............................................................. 145
7. Subject 4, image alone, 5-21-95 .................................................................. 146
8. Subject 4, image together, 4-30-95 ............................................................. 147
9. Subject 5, image alone, 8-20-95 .................................................................. 148
10. Subject 5, image together, 8-27 -95 ............................................................. 149
11. Subject 6, image alone, 1-21-96, drawing 1 .............................................. 150
12. Subject 6, image alone, 1-21-96, drawing 2 .............................................. 151
13. Subject 6, image alone, 1-21-96, drawing 3 .............................................. 152
14. Subject 6, image alone, 1-21-96, drawing 4 .............................................. 153
15. Subject 6, image alone, 1-21-96, drawing 5 .............................................. 154
16. Subject 6, image alone, 1-21-96, drawing 6 .............................................. 155
17. Subject 6, image together, 1-14-96, drawing 1 ......................................... 156
18. Subject 6, image together, 1-14-96, drawing 2 ......................................... 157

CHAPTER I
PROLOGUE

Introduction

Many people with health problems use therapies that do not conform
with the standards of the medical community. Although most physicians are
unaware of its popularity, alternative medicine is being used world-wide
(Murray & Rubel, 1992). There is strong evidence that people in every social,
economic, and educational class seek and use alternative care (Murray &
Rubel, 1992). In a recent survey, thirty-four percent of the respondents
reported using at least one unconventional therapy during the past year, and
seventy-two percent of those did not inform their medical doctor about the
use of these therapies (Eisenberg et al., 1993). The types of alternative ther-
apy sought were relaxation techniques, chiropractic, massage, imagery, spiri-
tual healing, commercial weight-loss programs, macrobiotics, herbal
medicine, megavitamin therapy, self-help groups, energy healing, biofeed-
back, hypnosis, homeopathy, acupuncture, folk remedies, exercise, and
prayer. It was discovered that, in particular, unconventional treatment is
sought by cancer patients (Eisenberg et al., 1993). Of those who used uncon-
ventional therapies, thirteen percent used relaxation techniques, twenty-five
percent prayer, four percent imagery, one percent biofeedback and one
percent hypnosis. The authors concluded that unconventional medicine has
an enormous presence in health care delivery in the United States. The
number of visits made to providers of unconventional therapy was greater
than the number of visits to primary care medical doctors nationwide

(Eisenberg et al., 1993). Recently, the United States Department of Health

2
and Human Services has recognized the need for scientific understanding of
these phenomena by establishing in the National Institutes of Health a new
Office of Alternative Medicine.

Imagery is one form of an alternative and usually adjunctive approach
in the treatment of cancer. The possibility of influencing the course of cancer
through imagery as well as other behavioral interventions is an exciting
notion which is receiving attention in the psychoneuroimmunology literature
(Gruber et al., 1993; Kennedy, Kiecolt-Glaser, & Glaser, 1988; Kiecolt-Glaser
& Glaser, 1995; Ratliff-Crain, Temoshok, Kiecolt-Glaser & Tamarkin, 1989;
Zachariae et al., 1994).

A preliminary study to assess the efficacy of conjoint (spousal) imagery
versus individual imagery by breast cancer patients is described in this
dissertation. The research involved a volunteer group of premenopausal
women who have had surgical intervention and completed a standard proto-
col of chemotherapy (and, in one case radiation therapy in addition) for breast
cancer, as well as their husbands. At the first meeting of each couple with
the experimenter, blood was drawn from the woman (to establish baseline
blood counts), a medical history of the patient was obtained, and the partners
independently completed the Dyadic Adjustment Scale (Spanier, 1976)
questionnaire (which was later used to evaluate the marital relationship).
The wife and husband then watched a fifteen minute educational video
(Bioimagery, 1993) about the body's immune system. At this session the
researcher informed the couple that she was particularly interested in T-cells.
Later the participant was on two occasions guided in immune system
imagery, once alone and once with her co-imaging spouse. Blood was drawn
from the female participant before and thirty minutes after (Achterberg,
1993) each of the two succeeding weekly imagery sessions, for the purpose of

3
comparing the blood assays. The participants drew on paper their imagery
experience in the intervening thirty minutes following the immunoimagery.
After venipuncture of the woman, they were interviewed about their
drawings, according to the IMAGE-CA protocol (Achterberg, 1985). The pre-
and post-treatment blood counts for each imagery session were compared, as
were the baseline and pre-imagery numerosities and the post-treatment
levels. The relative vividness and strength of the pictorial representations of

the cancer and the white blood cells were assessed independently by two

raters, according to the IMAGE-CA scale (Achterberg, 1985).

Background

"Imagery is the thought process that invokes and uses the senses;
visual, auditory, smell, taste, the sense of movement, position, and touch. It
is the communication mechanism between perception, emotion and bodily
change." (Achterberg, 1985) Imagery techniques allow a person to experience
an imaginative transformation that carries with it the energy to induce
change. A most remarkable feature of imagery work is that it can be accom-
panied by physiological changes. This has been quite powerfully noted in the
professional literature by numerous practitioners (Achterberg, 1985; Benson,
1975; Dossey, 1989; Epstein, 1989; Siegel, 1986; Siegel, 1989; Simonton,
Matthews-Simonton & Creighton, 1978). This connection is astonishing only
in the context of the past three hundred years of Western medicine. In
ancient societies health practitioners assumed the mind and body to be inti-
mately intertwined (Achterberg, 1985). Recently, the field of psychoneuro-
immunology has begun to explore the connections between the mind and the

workings of the immune system. Convincing evidence exists that the mind

4
and body are one unified system (Pelletier & Herzing, 1988; Pert, 1997; Rossi,
1993;)

Rossi (1993) writes: “Mind and body are not separate phenomena, one
being somehow spirit and the other matter. Mind and body are both aspects
of one information system. Life is an information system. Biology is a
process of information transduction. Mind and body are two facets or two
ways of conceptualizing this single information system.” Rossi goes on to
describe that the two fundamental processes of mind-body communication
and healing to be mind-body information transduction and state dependent
memory, learning and behavior, with the limbic-hypothalamic system being
the major transducer. He believes state-dependent memory, learning and
behavior phenomena to be the “missing link” in all previous theories of mind-
body relationships. Following Selye, Rossi regards stress to be an important
consideration in this model. Figure 1 illustrates Rossi’s view of the entire
process of mind-body information transduction. Figure 2 depicts some of the
cells in immune system that are responsive to stress and psychosocial cues
(Rossi 1993).

Our persona is multifaceted. We not only have minds that can affect
our bodies, but bodies that respond to drugs, surgery, radiation, and other
medical treatments. It is important to recognize the complementarity of the
roles of the treatment of the mind (emotions, attitudes, feelings, and
perceived meanings) and physical treatment of human illness. We need a
balanced approach that considers the mental and physical aspects of human

nature when illness arises.

Figure 1. Schematic representation of the role of the limbic-hypothalamic
system on the autonomic, endocrine and immune systems.
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9

In 1988 researchers studied ten adults with metastatic cancer for one
year, drawing monthly samples of blood (Gruber, Hall, Hersh & Dubois,
1988). The subjects were at the same time regularly engaged in relaxation
and guided imagery. Several measures of immune system function, including
T-cells, were found to be significantly elevated in the direction of enhanced
activity in a fifteen month time span. It was concluded that relaxation and
imagery can measurably affect immune responsiveness. Several weeks to
months elapsed for immune system changes to reach statistical significance.
The authors suggest that measures of T-lymphocytes may be more reliable
outcome measures than other indices (Gruber et al., 1988).

Immunological responses of thirteen women with Stage I breast cancer
(lymph node negative) who were healthy at the time of training were studied
over a period of eighteen months (Gruber et al., 1993). The researchers found
T-cell measures to be very responsive to behavioral interventions over time,
and suggest that the efficacy is cumulative. Their results Show statistically
significant effects, primarily on T-cell and natural killer (NK) cell popula-
tions. Although behavioral interventions correlated with immune system
measures, no significant psychological changes were detected except a reduc-
tion in anxiety (Gruber et al., 1993).

Investigations have also revealed that stress hormones, such as cortisol
and the catecholamines (epinephrine and norepinephrine), decrease after
relaxation (Jemmott & Locke, 1984). Because our immune defenses tend to
weaken when we generate stress hormones, relaxation exercises may be one
way to maintain healthy resistance. Stressed medical students (Kiecolt-
Glaser et al., 1984) had decreased levels of helper T-cells on the day of exam-

inations. When half the subjects were taught relaxation exercises, their T-

10
cells increased in number. The percentage increase of T-helper cells could be
predicted by how frequently the students practiced relaxation.

Cacioppo and his colleagues (1995) evaluated the effects of brief
psychological stressors in older women. They noted heightened autonomic
activation, elevated sympathetic adrenomedullary activity and an effect on
cellular immune response as evidenced by increased circulation of NK cells,
increased NK cell lysis, and a decreased blastonogenic response to the mito-
gen Con A, and increased CD8 cell numbers.

Equally strong evidence of the connection between coping style and
immune suppression comes from studies involving natural killer cells, which
kill cancer cells that are trying to spread through the bloodstream. For
example, breast cancer patients who appeared "adjusted" to their illness --
passive, fatigued, apathetic -- and who were lacking in social support were
found to have more cancerous lymph nodes and weaker NK cells than those
who complained more, had difficulties accepting their prognosis and had
stronger social support (Levy, Lippman, & Terry, 1980; Levy, Herberman,
Maluish, Schlien, & Lippman, 1985).

In a longitudinal study of 280 men and women, those who presented a
pessimistic style of thinking had significantly lower immune function in both
T-lymphocytes and N K cells (Seligman, 1986). Locke and co-workers
reported lower levels of immune function, including NK activity, among
depressed or anxious persons (Locke et al., 1984). Both depression and
reduced NK activity have been associated with cancer (Levy et al., 1985;
Shekelle et al., 1981).

Research on the effects of social support and the prognosis for women
with breast cancer indicates consistently positive correlations. Supportive

friends and a strong social support network clearly contribute to length of

11

survival. Conversely, social isolation and lack of social support (in addition to
apathy and unhappiness) were predictive of a lowering of the natural killer
cells and a poor prognosis in early stage breast cancer (Levy, Herberman,
Lippman, & d’Angelo, 1987; Levy et al., 1985; Reynolds & Kaplan, 1986).
Cohen et a1. (1997) found more diverse social networks to be associated with
greater resistance to upper respiratory illness. They found the number of
social connections to be much less important than gathering support from a
variety of sources (Cohen, Doyle, Skoner, Rabin, & Gwaltney, 1997).

David Spiegel and his colleagues have done a carefully controlled longi-
tudinal study of support group intervention with women who have metastatic
breast cancer (Spiegel, Bloom, Kraemer, & Gottheil, 1989). They found the
pain experiences between the treatment and the control groups to be signifi-
cantly different. The sensation of pain increased over time in the control
group and decreased in the treatment group. Retrospectively, the authors
examined the state death records on the 86 patients who had participated in
the study and found that after 48 months all the control patients were dead
while a third of the treatment patients were still alive. When the mean
differences in survival time of the two groups were compared, the investiga-
tors found the treatment group lived twice as long as the control group
(Spiegel et al., 1989).

The immediate and long-term effects of immune function were evalu-
ated for participants in a six-week structured psychiatric group intervention
for patients with malignant melanoma (Fawzy et al., 1990). The results
suggested that short-term psychiatric group intervention was associated with
longer-term changes in affective state, coping, and the natural killer

lymphoid cell system (Fawzy et al., 1990).

12

Levy and her colleagues found that a significant amount of natural
killer activity variance in Stage I and 11 breast cancer patients could be
explained by five variables (Levy et al., 1990). Higher NK activity could be
predicted by the perception of high-quality emotional support from a spouse
or significant other, perceived social support from the patient's physician,
estrogen receptor-negative tumor status, having an excisional biopsy as
surgical treatment, and actively seeking social support as a major coping
strategy (Levy et al., 1990). The most important predictor was the perception
of emotional support from a spouse or significant other.

Esterling, Kiecolt-Glaser and Glaser (1996) explored the cellular and
psychological mechanisms that might explain the observed changes in natu-
ral killer cell cytotoxicity associated with chronic stress. Former and current
spousal caregivers of patients with Alzheimer’s disease showed higher
enriched natural killer cell responses to two cytokines when heightened levels
of positive emotional and tangible social support were present.

Stress and social support have been found to modulate biological
responses in normal healthy adults (Glaser et al., 1992). Both stress and
social support were related to medical students’ ability to generate an anti-
body response to the hepatitis B inoculation. Students who were more
anxious and more stressed Showed a delay in seroconversion. Those who
reported less social support had a poorer immune response to the HBsAg as
shown in the combined measure for antibody titers and the T-cell response.

In women, the immune system seems to be sensitive to the social
support that comes from a good marriage. In a study of 38 married women,
researchers found that marital quality was significantly associated with
immune functioning, including percentage of helper T-cells and ratio of

helper to suppressor lymphocytes. The women who perceived their marriages

13

as satisfying and supportive had less depression and loneliness, as well as
better immune defenses. The quality of a person’s marriage as perceived by
the subject was a more powerful predictor of happiness than work satisfac-
tion or relationships with friends (Kiecolt-Glaser, Fisher, et al., 1987).
Relatively few marriages (7%) end in divorce after a diagnosis of breast
cancer. It is possible that women react less intensely and less pessimistically
to stressful events if they know they have someone to share them with who
will support and help them. However, it is difficult to know whether those in
a satisfying marital relationship are less prone to depression and loneliness
or whether the absence of loneliness and depression in marital partners
makes the marriage more satisfying or whether a separate third factor is
involved.

Research over the years has suggested that when we mentally picture
our bodies doing something, internal changes occur accordingly (Achterberg,
1985). If one rehearses a strenuous dance routine mentally, s/he evokes
muscular changes, increased blood pressure, altered brain waves, and activa-
tion of sweat glands. MSU researchers have demonstrated the effect of
imagery on neutrophils, which are white scavenger blood cells that are impor-
tant in keeping us free from infection (Schneider, Smith, & Whitcher, 1983).
They concluded people who are ill need to know some specifics about their
illness and the body's natural defenses and convert the knowledge into some
type of image in order to have an impact on cellular activity and function.
They determined that relaxation training is a factor in making the imagery
effective. In a separate study, they also found educating subjects about
lymphocyte function and relaxation, and training them to do guided imagery,
may have influenced changes in numerosity of T-helper and T-suppressor

lymphocytes (Schneider, Smith, Mining, Whitcher, & Hermanson, 1990).

14
Biofeedback research has shown if we imagine hot scenes (sun, beach, desert)
we can increase blood flow and warmth of our hands and other parts of the
body (Schwartz, 1984).

The evidence suggests that people may also be able to alter their
immune systems and disease states by what they imagine and visualize. In
other words, people may be able to activate self-healing systems.
Researchers have found that how a group of cancer patients pictured their
malignant cells and their immune systems was the most powerful predictor of
their disease status two months later (Achterberg, et al., 1977). Those with
the best outcome visualized their white blood cells as being like Vikings or
other legendary figures who fought for God and country. Those with poor
outcomes pictured their immune cells as weak or soft, like snowflakes or
clouds.

Trestman (1981) analyzed the color of cancer patients' images of their
cancer. Thirteen of the fourteen people with "good" status imagined their
cancer as red or black, while eight out of eleven with relatively poorer status
described their cancers as lighter colors.

Thus there is a growing body of research that documents the interrela-
tionship between mind and immune system function, the importance of social
support to breast cancer patients, and the impact of imagery upon personal
health. Imagery techniques are rarely credited as being essential to the prac-
tice of technological medicine, but they are considered useful to the psycho-
logical well-being of patients. Imagery's beauty is multifaceted: it is afford-
able by the masses, and indeed it has been used in folk healing for centuries;
it is a tool whose power has been documented throughout history. Imagery is
a powerful, non-invasive, self-controlled, inexpensive method of healing. In

order for imagery to move from an adjunctive role to an essential factor in

15
healing, a solid, convincing body of research must be conducted to support the

role of imagery in total health.

Problem Statement

The purpose of this research was to study the effects of imagery and
active spousal support on blood counts in women with diagnosed
premenopausal breast cancer who had followed a standard course of treat-
ment. The subjects utilized guided immunoimagery, alone and with their
spouse, to help the researcher ascertain whether imaging with a partner
results in more salutary effects. For thirty minutes after each imagery
session, prior to venipuncture, the woman or the couple was asked to draw a
picture of the cancer and their corresponding immunoimages. The pictorial
accounts were assessed relative to the patient's and spouse's attitudes about
the virulence of the cancer, and the power of the patient's immune system.
The drawings were rated according to the IMAGE-CA developed by
Achterberg and Lawlis (1984); the relative strength and vividness of the
depicted cancer cells and immune system cells were related to the efficacy of
the blood count changes. The couples' satisfaction and adjustment within the
marital relationship (DAS scores) was an intervening variable for both the

solitary and conjoint imagery and the subsequent blood assays.

CHAPTER II
REVIEW OF THE LITERATURE

Imagery is a multisensory representation of an experience. Some
think of imagery as a waking dream or similar to daydreaming. It is
produced by imagination. Individuals generally utilize one or two senses as
primary sources for experiencing their world. For example, musicians
frequently are very aware of the presence or absence of sound in their envi-
ronment; they are often keenly aware of auditory change.

Imagery can be guided or spontaneous. Therapeutic guided imagery
includes an induction, a phase of involvement or disassociation, and a rever-
sal. Either purposefully guided or spontaneously experienced, imagery is
embedded in a sensory environment: visual, auditory, tactile, olfactory, taste,
or kinesthetic. It is limitless and can embrace the impossible -- flying, living
in a different time and space, communicating with animals or inanimate
objects. Although many people associate imagery with memory representa-
tions of physical objects, non-physical processes, such as mathematical prob-
lems and logical relationships, can also be represented in imagery.

Imagery is often used to distract (escape from pain, stress, or problems)
or to focus (explore an issue in depth for the purpose of understanding or
changing it) (Schneider, 1987 -1991). Distraction allows an individual to avoid
pain and anxiety for a time. It provides short-term relief and is often used in
pain management. Focusing is a technique described by Gendlin (1978) that
facilitates the development of a felt sense -- a contact with a special kind of

internal body awareness. A felt sense is body and mind before they are split

16

17
apart, the body's knowing what a person's problems feel like. Gendlin (1978)
believes that the body finds its own way in providing answers to many prob-
lems. Focusing is being studied in relation to issues such as spirituality,
business, problem-solving, creative writing and dreams.

End result imagery occurs when the objective of induced imaging is the
individual seeing the desired result as having already occurred; the method is
utilized by Simonton, Matthews-Simonton, and Creighton (197 8) in Mg
WelLAgafm. Process imagery occurs when the individual images a specific
desired effect; it is illustrated by Bernie Siegel's (Siegel, 1986) healing

imageries in Love Medicine & Miracles.

 

Dendinger and Trop (1979) purport imagery to flow out of movement
and movement to flow out of imagery. "The body's motional usages of and
emotional attitudes toward space, time, energy and rhythm can engender in
the imagination deep images. The way we move within ourselves, gesture,
posture, touch, or the way we move and are moved in relationship to another
person contains the non-verbal movement history of our way of being in the
world."

Mental imagery is non-logical. Epstein (1989) defines mental imagery
as the mind thinking in pictures, and terms it the thinking used for making
contact with our inner subjective reality. According to Achterberg (1985),
"Imagery is the thought process that invokes and uses the senses; visual,
auditory, smell, taste, the sense of movement, position, and touch. It is the
communication mechanism between perception, emotion and bodily change.
A major cause of both health and sickness, the image is the world's oldest and
greatest healing source."

In ancient societies the mind and body were believed to be intercon-

nected. In the realm of shamanism, nothing exists in isolation; shamanism

18

speaks to the unity of all things and all beings (Kalweit, 1988). Epstein notes
that no medical system in the history of the world, including Western
medicine prior to the seventeenth century, has distinguished the mind as
separate from the body (Epstein, 1989). The emerging field of Behavioral
Medicine (psychoneuroimmunology) is exploring the connections of the mind
and body (Achterberg, 1985; Achterberg, Lawlis, Simonton, & Simonton,
1977; Holden, 1978; Palmbad, 1981; Rogers, Dubey, & Reich, 1979; Schneider
et al., 1990; Sklar & Anisman, 1979; Solomon, 1985; Solomon & Amkraut,
1981).

Locality has become a premise in modern medicine; healers must be by
the bedside or in the operating room. Yet acknowledgment of the role of mind
expansion and spirituality in the healing process is powerfully present in the
scientific literature. Spiritual beliefs and faith often are connected to the
concept of a Higher Being in the role of a spiritual guide. Prayer is directed
to an imaged entity who has not been confined to a specific place and the
prayer is often an image of the process or result desired by the praying
individual.

Dossey (1989) advances the concept of nonlocality in healing. He
regards the body as a local entity that can be treated with physically based
approaches anchored in the present, and the mind as nonlocal (infinite in
time and space) and also capable of bringing about profound changes in the
body. Dossey regards the best therapeutic reality as one that combines both
approaches.

Guided imagery encourages the user(s) to let go of extraneous, distract-
ing thoughts and to focus exclusively on the desired purpose of the imagery.
It is similar to the meditative state utilized in prayer. Transpersonal

imagery, where one's purpose is to affect another, closely parallels praying for

19
another's healing. Despite objections from both scientists and religionists,
researchers have sought evidence for prayer's power for more than a century.

According to healers who employ prayer routinely, it is more effective
for some problems than others. Therapies should be judged according to the
effects that occur in the conditions to which they are applied, and prayer is
not an exception. More than one hundred experiments, many conducted
under stringently controlled laboratory conditions, are reported in the scien-
tific literature, over half showing that prayer engenders significant changes
in both animals and human beings (Dossey, 1993). The effects of prayer were
not dependent on the praying person being physically proximate to the organ-
ism being prayed for. Even when an object was placed in a lead-lined room or
in a cage that shielded it from all known forms of electromagnetic energy, the
treatment effect still occurred (Dossey, 1993).

Descartes equated the mind with the brain, and Western thought
expects the mind to have locality, specifically in the brain. However, scien-
tific evidence is beginning to support the nonlocality of the mind. Brainlike
tissue is found throughout the body; chemical endorphin receptors have been
discovered outside the brain, in white blood cells and in the gastrointestinal
tract. Healers who employ a more holistic conceptualization would say mind,
body and world are one. Via computer-controlled experiments, minds have
communicated complex messages over long distances (Jahn & Dunn, 1987 ).
There was no difference whether the subjects were separated by one block or
by thousands of miles. If minds are not confined to time or space, they are
unbounded.

Throughout the body, the systems most important to our health -- the
brain, the glands, and the immune system -- connect and communicate via

messenger molecules that are sensitive to our cognition (Pert, Ruff, Weber, &

20

Herkenham, 1985). Pert sees the "body as the outward manifestation of the
mind" and body and mind as inseparable (Pert, 1986). For Pert, evidence of
that inseparability came from the discovery that a number of the chemical
messengers in the body are the same as the ones in the brain. Among these
"communication molecules" are neuropeptides that regulate our moods and
emotions. Because some can be found in the intestines as well as in the
limbic system (our feeling brain), Pert says, "the emotions are not just in the
brain, they're in the body."

In W
Health, Justice explores the key role of recently discovered neurotransmit-
ters, brain hormones, and other chemical messengers in the functioning of
the body and some of the ways by which these powerful molecules play a part
in illness and disease (Justice, 1987). How the chemical messengers are
affected by our attitudes and reactions to stress is examined. Justice traces
the pioneering work on "giving up" as a major factor that precipitates illness,
and looks at how some people postpone their deaths until after some special
occasion. He suggests positive attitudes and beliefs can protect and restore
our health by turning on self-healing systems.

The biopsychosocial risk-factor concept of disease differs from germ!
virus theory, the traditional medical model that suggests that pathology is
mostly caused by foreign forces that attack our bodies (Engel, 1960). Mech-
anisms of disease are connected with the brain and seem to be influenced by
our mental processes. Messages sent by brain cells in the form of neuro-
transmitters, hormones and neuropetides are received by cells that have
receptors all over their surface membranes. Our thoughts and perceptions

direct the chemical messages the brain sends.

21

Kendall (1981) reports that the mentally retarded and emotionally
disturbed population has a selective protection from cancer and autoimmune
disorders such as rheumatoid arthritis. The percentage of deaths from cancer
for the mentally handicapped is approximately 4%, in contrast to a 15-18%
incidence among the general population. As the mentally handicapped
subjects approach normal intelligence their cancer rate also increases
(Kendall, 1981). Achterberg (1985) notes that the criminally insane also
seem to have a low incidence of cancer, despite heavy smoking. She specu-
lates that there is a relationship between the diverse mental conditions of
mental retardation and emotional disturbance, both of which indicate a lack
of awareness of information from the environment. Is stress managed in a
way that does not inhibit the immune system in these populations?

Hope and belief in becoming well stir the imagination into action.
There are countless anecdotal accounts of an individual's images contributing
to a premature death or to a prolonged life despite the presence of grave
health issues. Achterberg (1985) describes the murderous power of images of
a woman diagnosed with early stages of breast cancer, who died within hours,
apparently from the workings of her imagination. Another such example is
the woman who was brought to a hospital comatose, paralyzed and diagnosed
with a massive brain tumor (Achterberg, 1985). A surgeon removed as much
of the tumor as safely possible, and since the patient was close to death,
neither radiation nor chemotherapy was attempted. The word tumor did not
imply a deadly cancer to the woman; her images were of recovery, not death,
and she defied the odds.

In the introduction to Normal Cousins' book, The Healing Heart
(1983), Bernard Lown describes a critically ill patient whose cardiac muscle

was irreparably compromised and for whom no further treatment was known.

22

During rounds Dr. Lown mentioned to the accompanying staff that the
patient had a "wholesome gallop" (indicative of a failing heart). Several
months later the patient, who had made a remarkable recovery, told Dr.
Lown that he knew when he heard that his heart had a wholesome gallop
that he must have a lot of kick left in his heart and therefore could not be
dying. He reported that he knew instantly that he would recover. The words,
wholesome gallop, conveyed to the patient an image of a horse that was full of
life, and this image was credited by his physician as being responsible for his
new state of health.

Several investigators have ingeniously attempted to measure mystical
elements in healing. Prayer has long been held by most religions to contain
powerful healing properties and throughout time it has often been used as a
form of intervention in illness.

Experiments have been carried out to address such questions as: Is
spiritual healing real? Does prayer work? Is there an effect that can be
measured and reproduced? In one study, the effects of prayer on patients in a
coronary care unit were followed by Randolf Byrd (1988) in a randomized
double blind experiment over a ten-month period. Neither the patients, the
nurses, nor the doctors knew which group a given patient was in. Roman
Catholic and Protestant prayer groups were recruited around the country to
pray for the 192 members in the designated group; 201 patients in the control
group were not prayed for. Members of prayer groups were given the names
of the patients, told something about their condition and instructed to pray
for them, but were not told how to pray. Each person prayed for many differ-
ent patients, and each patient in the experimental group had five to seven
people praying for him or her. The prayed-for patients were five times less

likely to need antibiotics than the control group; they were three times less

23
likely to develop pulmonary edema; none required endotracheal intubation;
and few patients in the prayed-for group died (although that difference was
not statistically significant). The geographical distance between the patient
and the person praying did not seem to matter; prayer groups around the
corner from the hospital were not found to be more effective than ones
hundreds of miles away.

A careful set of experiments involving the survival and growth of
seeds, known as the Spindrift project, also sought to answer the question: Is
prayer more powerful if a specific goal is prayed for or if the person praying
simply asks: "Thy will be done"? Although both approaches worked, nondi-
rected prayer was found to be quantitatively much more productive (Dossey,
1989). Dossey writes: "Thus, after many years of research, the Spindrift
researchers have formulated the law of the conceptual whole: So long as the
practitioner can hold in his mind an overall concept of the system involved,
the effect of prayer is constant over all components." Seemingly in contrast,
researchers and practitioners who employ imagery as a tool for healing have
observed more powerful effects when the patient had knowledge about the
disease and the healing process (Achterberg, 1985; Schneider et al., 1990).

Hemolysis is the process of putting red blood cells in a dilute solution
where they are stressed and gradually swell and burst, thereby leaking their
hemoglobin into the solution. Hemolysis can be measured with extreme
accuracy with a spectrophotometer. William Braud (1990) used hemolysis to
investigate whether ordinary people could mentally protect red blood cells
from seriously stressful influences. Further, he wondered if mental protec-
tion could be done at a distance and whether it works better on a subject's
own red blood cells or if it equally protects the cells of others. Thirty-two
untrained subjects (17 female, 15 male) mentally attempted to keep red blood

24

cells from dissolving when the cells were placed in a dilute solution in a test
tube in a distant room. In a double blind design, about half were trying to
protect their own cells and the other half were protecting cells of another
person. A session consisted of two control and two protect periods, each
fifteen minutes long. When asked to protect the blood cells, the subjects were
shown a color slide projection of healthy red blood cells. In the control period
the subject was asked to think about matters not connected with the experi-
ment. The technician performing the post-measure did not know if the blood
originated from the subject or someone else, or whether the session was the
treatment or control condition. The author concluded from this study that
the subjects could affect rate of hemolysis. He also found healing thoughts
could function at a distance and that they seem to occur regardless of
whether they are directed at oneself or another. Although the overall effect
was unselfish, there also appear to be individuals whose healing thoughts
may be more potent for themselves than for others (Brand, 1990).

Braud and Schlitz (1983) studied the ability of 62 people with no
special characteristics to influence the physiology of 271 subjects who were
isolated from the influencers. Of the 271 subjects, the only participants espe-
cially recruited were a group of those in need of a "calming" influence on their
physiology. These subjects had evidence of greater than usual sympathetic
autonomic activation, as evidenced by stress-related complaints, excessive
emotionality, excessive activity, tension headaches, high blood pressure,
ulcers, or mental or physical hyperactivity. These special subjects were
screened and it was confirmed that they indeed exhibited greater than aver-

age arousal of the sympathetic nervous system. There were thirteen experi-

ments in all.

25

The subjects were attached to sensitive instruments that measured
their electrodermal activity (the ability of the skin to conduct an electrical
current, which is an indicator of activity of the sympathetic part of the auto-
nomic nervous system). At a given signal, the influencer would try to exert a
calming or activating influence on the distant subject, who was unaware of
when the attempt was made. During influence periods the influencer used
mental imagery and self-regulation techniques to induce relaxation or activa-
tion in him or herself, while imagining an intended corresponding change in
the distant subject. Then the influencer would imagine the desired pen
tracings of the polygraph.

The effect was consistent, reliable and robust. In some experiments
the subjects reported spontaneously receiving the exact image of the influ-
encer. Braud and Schlitz (1983) concluded the transpersonal imagery effect
compared favorably with the magnitude of the influence on one's own physi-
ology; that the ability is widespread in the population; that transpersonal
imagery can occur at distances of 20 meters (greater distances were not
tested); that subjects for whom the influence would be beneficial were more
susceptible; and that this effect can occur without the subject's knowledge.

Most physicians respect the patient's will to live and realize their
treatments are far more effective if their patients believe in them (Schneider,
et al., 1990). Achterberg and Lawlis attempted to quantify beliefs and atti-
tudes about cancer with "disease imagery" ratings objectively obtained from
patients (Achterberg & Lawlis, 1978). They investigated the potential of
imagery as a predictor of survival time in critically ill cancer patients. A
projective instrument has been developed, called the IMAGE-CA (Achterberg
& Lawlis, 1979) to assess patients' attitudes about their disease and treat-

ment, as well as the belief that they may have innate ability to overcome the

26

illness. While developing their measure of disease imagery, Achterberg and
Lawlis (1979) administered a battery of psychological tests and obtained
informal disease imagery ratings for 126 cancer patients who had a five
percent chance of five-year survival. The results indicated that disease
imagery was a far better predictor than any standard psychological instru-
ment or blood chemistry for predicting disease status. In a validation study
of the IMAGE-CA with three separate groups of cancer patients having a
total number of about 200, Achterberg (1985) reported that the instrument
predicted with 93% certainty who would be in remission, and forecast an
unfavorable prognosis (death or significant deterioration within a two-month
time period) with 100% accuracy.

Lydia Temoshok and Henry Dreher (1992) speculate that living in a
state of chronic emotional repression may contribute to physiological illness.
They identify a Type C personality, "nice" people who do not express anger or
fear about their own well-being. For these people, emotional pain seems to be
entirely repressed. Type Cs appear patient, unassertive and cooperative.
The authors hypothesize that the chronic emotional repression may overtax
the brain, weaken the immune system and leave one more vulnerable to
disease. They suggest that since Type Cs neither fight nor flee when faced
with stress; they may overproduce opiates and adrenaline, both of which can
suppress the immune system. Temoshok's research on melanoma patients
revealed that patients who expressed their emotions more openly had more
cancer-fighting immune cells gathering around their tumors. Conversely,
patients who repressed their emotions had far fewer localized cancer-fighters

(Temoshok & Dreher, 1992).

27

The following immune system information has been summarized from
Jeanne Achterberg's description (1985):

The hypothalamus serves as an important regulator in the immune
system and it is intimately connected to the parts of the brain that are involved
in emotion (i.e., the limbic system). The limbic system forms a connecting
network with the frontal lobes, which are the most evolved part of the cortex
itself and believed to be critical for imagery and for planning for the future.

Neutrophils are white blood cells that are given life in the bone marrow,
and constitute about 65% of the total white blood cell (WBC) population. They
are chiefly responsible for fighting infections. They circulate, daily, looking for
bacteria that don't belong in the body. Neutrophils respond to chemicals that
are released at the injury site and prepare to attack by changing into a shape
that can more easily pass through blood vessel walls. They adhere to capillary
walls that have become sticky, and extend a small foot (pseudopod) through
any gap and slither out of the blood steam. Neutrophils then move toward the
offender and begin the process of destroying the intruders, which is called
phagocytosis. The engulfment and digestion are accomplished when the
neutrophil sends its cytoplasm flowing around the foreign particle, and then
isolates it in a sac (phagosome). Enzymes are then shot into the sac, and the
intruder is destroyed. Neutrophils are the first line of defense and are
followed by other specialized attackers.

B-cells and T-cells are WBCs called lymphocytes, because they circulate
through the lymph fluid. Both respond only to certain microorganisms, and
both are given their life in the embryonic bone marrow. The T-cells migrate to
the thymus, where they are energized for action. There are at least three kinds
of T-cells: the killers, whose specialty is killing viruses and foreign tissue

with potent chemicals; the helpers, which assist the B-cells in going after

28
their highly specific targets; and the suppressors, which serve a regulatory
function, perhaps keeping the immune system from going wild and attacking
self as well as nonselfi The killer T-cells are known to be involved in the
defense against cancer, the suppressor Ts in the prevention of autoimmune
breakdown. The B-cells create proteins called antibodies that can identify a
specific invader and start the complicated process of destruction.

The white blood cells, the chief representatives of the immune system,
have an uncanny ability to sort out friend from foe. Immunization through
vaccination is based upon the natural ability of the body to learn to defend
itself When the skills of the white blood cells go awry, autoimmune disease,
such as rheumatoid arthritis and multiple sclerosis, as well as infectious
disease and cancer, are possible. What the immune system doesn't already
know or hasn't learned from vaccination, it may be able to learn in another
way. All the biochemical changes that happen during the real-life exposure
may occur during fantasy.

Cancer can be viewed as a disease of the immune system. Particular
white blood cells, T-cells, identify and destroy the cancer cells they meet in the
body. Following the destruction of cancer cells, the macrophages join the scene
and digest any remaining pieces.

The surveillance theory of cancer suggests that just as our bodies often
house the potential for strep and staph infections, they also have malignant
cells that are held in check by the T-cells and macrophages. According to this
theory, when these cells fail to recognize and kill the malignant cells, the
cancer cells multiply and become a tumor.

The numerous accounts of spontaneous remission (the disease goes
away in the absence of medical treatment) support the idea that the body has
an ability to heal itself

29

With most autoimmune disorders, as well as with cancer, cytotoxins
(cell poisons) are sometimes a successful treatment. They are believed to stop
the multiplication of the offensive cells, which seem to be more susceptible to
the poison than healthy cells. It is conceivable that chemotherapy could be
paired with a neutral substance and the immune system could be conditioned
to respond to the administration of the neutral substance alone as it did when
it was paired with the cytotoxin, thereby eliminating some of the toxic efiects of
the drug.

It has been noted that alpha-endorphins and other opioid peptides,
secreted under inescapable stress, suppress the function of T-cells in the
immune system and reduce the effectiveness of natural killer cells (Shavit,
Lewis, Terman, Gale, & Liebeskind, 1984). [Natural killer (NK) cells are
other lymphocytes that do not require sensitization to express the killer func-
tion (Berkow, 1992).] A 1988 study of 312 patients revealed that those who
were repressing emotion had increased levels of opiates in their brains and
bodies (Jamner, Schwartz, & Leigh, 1988). These patients also possessed far
fewer white blood cells in their immune systems. The excess opiates, the
authors concluded, were a result of negative coping under stress and helped
suppress patients' white blood cells.

In a paper reviewing hypnosis as it relates to the body's immune
system, Hall hypothesizes that it should be possible to raise immune
functioning under controlled laboratory conditions and that hypnotizability
may be related to one's ability to enhance immune system functioning (Hall,
1982—1983). Voluntary self-regulation of immune responses through relax-
ation and imagery resulted in a statistically significant increase in one mito-
gen measure and a marginally significant increase in one of the blood count

measures. Age, hypnotizability, and their interaction Significantly predicted

30
changes in the set of blood count measures, but not in the set of mitogen
blood measures (Hall, Mumma, Longo, & Dixon, 1992).

Researchers studied 108 undergraduates who received immunizations
for swine flu (Locke, 1982). They found students who coped poorly with
stress had a significantly impaired immune response as measured by
decreased natural killer cells; the students with high stress and good coping
ability had the highest level of NK cell activity. In a later study, Locke noted
that when anxiety and depression were high, activity of natural killer cells
was low, suggesting possible lowered immunity (Locke et al., 1984).

Zachariae and his colleagues at the University of Denmark (1990)
measured the effects of relaxation and guided imagery on cellular immune
function on ten healthy subjects. When the subjects were instructed to
imagine their immune systems becoming very effective, a significant rise in
natural killer function resulted. The study does not indicate whether the rise
in NK activity is due to the relaxation, mental imagery, or both. No signifi-
cant differences could be detected for numerosity of mature T-cells, T-helper
cells, T-suppressor cells, B cells or monocytes. One hour following the
imagery most T-cell counts decreased from the baseline value. Since NK cells
may be a defense for newly formed neoplasias, these authors suggest relax-
ation and imagery might be beneficial for cancer patients with minimal
residual disease (Zachariae et al., 1990).

Another group of researchers led by Zachariae (1994) compared an
imagery group and a relaxation group to controls and found a decrease in
blood counts following the intervention in both the imagery and relaxation
groups, and no differences in pre and post blood draws in the control group.
No differences in natural killer cell activity were found between the imagery,

relaxation and control groups (Zachariae et al., 1994).

31

The literature that examines the impact of stress on immune system
measures in humans yields conflicting results, illustrating the complexities
encountered when researchers try to isolate variables and to control for the
myriad of influences (some positive, some negative, some long term, some
short term) on immune system functioning.

A study of first year medical students found that the stress of exams
caused a drop in both the number and activity of natural killer cells. Also,
extremely lonely students had the least active natural killer cells and weaker
immune systems (Kiecolt-Glaser et al., 1984). A reduction of interferon, an
important protein produced in the body that stimulates NK activity, was also
found in medical students during final examinations.

In a more recent study, students who were more anxious or more
stressed showed a delay in seroconversion to the Hepatitis B vaccine.
Following seroconversion, the students who reported less social support also
showed a poorer immune response to the vaccine, as measured by antibody
titers and T-cell response (Glaser et al., 1992).

A Canadian research group tested the effects of stress on medical
students on three occasions: one month prior to an exam (during a time of
low academic stress); immediately following an exam; and ten days following
the same exam (Dobbin, Harth, McCain, Martin, & Cousin, 1991).
Lymphocytes decreased significantly after the exam, and at the ten day post-
exam measurement they had not returned to baseline levels. Interferon-g
(IFNg) is mainly produced by T-lymphocytes (Benjamini, Sunshine, &
Leskowitz, 1996); therefore, the IFNg decrease noted in this study was consis-
tent with the decrease in lymphocytes. Interleukin-1 (IL-1) is a cytokine that
has a proliferative effect on T- and B-cells. IFNg and IL-1 impact the

immune response in an integrated fashion (Benjamini et al., 1996). Thus, the

32

significantly higher IL-1 levels measured after the exam were unanticipated
by the researchers, who concluded that this divergence between IFNg and IL-
1 production suggests that stress affects the IL-1 producing cells (e.g., mono-
cytes) in a very different way than it affects IFNg producing lymphocytes
(Dobbin et al., 1991).

Psychological stress may not suppress cellular immune function in all
individuals. T-suppressor/cytotoxic lymphocytes increased in number and T-
cell response to stimulation by phytohemagglutinin decreased after subjects
were exposed to a twenty minute laboratory stressor, but only for persons
who also Showed elevated catecholamine and cardiovascular reactions to
stress (Manuck, Cohen, Rabin, Muldoon, & Bachen, 1991)

An experimental group of healthy subjects who were trained in
biofeedback techniques exhibited Significant increases in blastogenesis (a
method used to assess lymphocyte competence), most notably the interaction
of a 100 ml suspension that contained 106 lymphocyte cells/ml with 100 mg of
phytohemagglutinin (PHA), when compared to untrained controls (McGrady
et al., 1992). Contrary to the documented suppression of immune function by
stress (noted earlier), biofeedback in this study increased responsiveness of
the lymphocytes to mitogenic stimulation.

Caregivers for close relatives with Alzheimer's disease who for years
put aside their own needs for the benefit of others suffered marked loss of
immune strength (Kiecolt-Glaser, Dura, Speicher, Trask, & Glaser, 1991).
Lymphocyte suppression during bereavement has been documented to occur
as early as one month following a death (Schleifer, Keller, Camerino,
Thornton, & Stein, 1983). Natural killer cell activity is much reduced in
grieving Spouses (Irwin, Daniels, Smith, Bloom, & Weiner, 1987). Kiecolt-
Glaser and Glaser (1992) in their review stated: “The weight of the evidence

33
to date suggests that chronic stressors are associated with continued down-
regulation of immune function rather than adaptation.” (Kiecolt-Glaser &
Glaser, 1992)

On the other hand, the ability to love and care about others seems to
result in lower levels of the stress hormone norepinephrine and a higher ratio
of helper/suppressor T-cells, an important balance in a healthy immune
system (McClelland, 1985). McClelland professes that the evidence strongly
suggests that love aids the lymphocytes and improves immune function.
People who are in love suffer fewer colds and have white blood cells that more
actively fight infections. Lovers were reported to have lower levels of lactic
acid in their blood, which means they are less likely to get tired, and higher
levels of endorphins, which may contribute to a sense of euphoria and a
reduction of pain (Siegel, 1986).

The interdependence of happiness and health was found to predict
longevity better than any health or physical activity factor in 268 subjects
who were controlled for effects of age, work satisfaction and happiness
(Palmore, 1969). Palmore also found marital satisfaction to be highly associ-
ated with both the level of immune functioning and psychological well-being.
A significant association between perceptions of life satisfaction and health
was also reported among different groups of men and women whose illnesses
were charted for more than twenty years (Hinkle, 1961). Ten thousand men
in Israel showed nearly two times lower risk for development of angina
pectoris if they answered "yes" to the question: "Does your wife show you her
love?" (Medalie & Goldbourt, 1976).

Some research has explained how immunity is impacted by our
thoughts, beliefs, and attitudes as well as by the social support we perceive

(Plaut & Friedman, 1985). In one example, Maddi and Kobasa (1984) found

34
that executives who were high in hardiness and low in illness used "trans-
formational coping" to deal with problems. They describe transformational
coping as the process of "altering the events so they are less stressful.” The
hardy executives thought about problems in optimistic terms and acted
decisively.

Support affects T-cells when a person is under stress. Kemeny (1984)
found lack of support to be related to a reduction of suppressor T-cells and
associated with recurrence of herpes simplex, Type II. When social contact is
increased or loneliness is reduced, the immune system seems to strengthen.
A group of thirty elderly people in retirement homes showed increased
immune function in terms of both NK cells and antibodies when they were
visited three times per week for one month (Kiecolt-Glaser et al., 1985).

Berkman and Syme tracked the health of people in Alameda County,
CA for nine years. They found those people with social ties -- married, having
friends, contact with relatives, participating in church affairs and belonging
to other groups -- had significantly lower mortality rates (Berkman & Syme,
1978). In another study, Reynolds and Kaplan studied 6848 Alameda County
residents over a period of seventeen years. Women who were socially isolated
and felt socially unsupported were found to be at a significantly higher risk of
getting cancer, and of dying from the disease. Social ties did not seem to
affect whether men got cancer. However, among men who got cancer, the
socially isolated ones died much sooner than those with social ties (Reynolds
& Kaplan, 1986). In a very recent publication the diversity of kinds of social
support as opposed to the numbers of supporters was found to be important
in resistance to upper respiratory disease (Cohen et al., 1997).

Thus, the notion that guided imagery can have a salutary effect for

women who have had breast cancer, and the hypothesis that conjoint imagery

35
with a supportive spouse can enhance the benefits, are well grounded in the
literature. A preliminary study of this proposition is described in this disser-

tation.

CHAPTER III
RESEARCH METHODOLOGY

Conceptual Model
Modern medical treatment is depicted by evolutionary

complexity. Intricate cancer protocols can be stressful for patients and their
families. There may be biopsies, blood chemistries, X-rays, surgery,
radiation, and chemotherapy. These procedures may be protracted,
frightening, painful, and difficult to endure. The breast cancer patient also
has to deal with her illness as well as with her altered roles as a career
woman, wife, mother, and sexual partner. She may be confronting her
mortality for the first time. There may be financial and psychological
stresses on the family as its members individually and collectively face the
multilayers of change and loss that occur with the diagnosis of breast cancer.

Engel speaks of the powerful influence of paradigms upon what scien-
tists select to study and how they pursue questions (Engel, 1992a). He
further points out that reliance on reductionistic thinking and Newtonian
and Descartes dualism automatically excludes that which is distinctively
human from the realm of science (Engel, 1992a). He acknowledges that
science is a human activity, and notes that what the scientist does cannot be
separated from the research question because every observation is predicated
on the scientist's decisions as to what s/he observes and how.

The biomedical model that has been prevalent in the Western culture
for the past 300 years accounts for disease by its biochemical factors without

considering social or psychological dimensions. The patient complains, at

36

37
times, that the disease is being treated rather than the person. The biomedi-
cal model separates mind from body. Reductionistic medical thinking is
particularly neglectful when the impact of non biological circumstances upon
biological processes is ignored.

In Medical Fail): Therapy (McDaniel, Hepworth, & Doherty, 1992), a
new paradigm for the biopsychosoical treatment of patients and their families
who are dealing with health issues is proposed. Systems concepts are applied
to patient care for a broad range of medical concerns. The concepts under-
lying this new paradigm are derived from the work of George Engel (Engel,
1960; Engel, 1977; Engel, 1980a; Engel, 1992).

In 1977 Engel proposed a biopsychosocial model for organizing and
delivering medical care; this classic article was reprinted in Family Systems
Medicine in 1992 (Engel, 1992b). Engel attributes medicine's crisis to adher-
ence to a model of disease no longer adequate for the scientific tasks and
social responsibilities of medicine. He contends that the biomedical model
does not include the human dimension and that the systems biopsychosocial
model successfully resolves this problem (Engel, 1980; Engel, 1985).

Rooted in general systems theory principles attributed to von
Bertalanffy, the biopsychosocial model acknowledges the hierarchical, inter-
dependent relationships of biological, psychological, individual, family, and
community systems. Within the framework of this model one can see how
multiple levels of systems are affected simultaneously. Any change in a part
of the organism impacts the whole person within the context of his family and
culture. All human problems are biopsychosocial systems problems: psycho-
social problems have biological features and biomedical problems have
psychosocial features (Engel, 1992b). Engel advocates the inclusion of

psychosocial systemic awareness without sacrificing the enormous advan-

38
tages of the biomedical approach and cautions the medical community that
absence of psychosocial awareness distorts perspective and interferes with
patient care (Engel, 1992b). He writes "psychophysiologic responses to life
change may interact with existing somatic factors to alter susceptibility and
thereby influence the time of onset, the severity, and the course of the
disease" (Engel, 1992b).

Engel presented the biopsychosocial model schematically by vertical
stacking to illustrate the hierarchy (see Figure 3) and by a nest of squares to
emphasize the continuum (see Figure 4) (Engel, 1980b). Engel notes the
individual person as the highest level of the organismic hierarchy and simul-
taneously the lowest level of the social hierarchy (Engel, 1980b):

Consideration of the hierarchy as a continuum reveals another obvious
fact. Each system is at the same time a component of higher systems (Figure
4). System cell is a component of systems tissue and organ and person.
Person and two-person are components of family and community. In the
continuity of natural systems every unit is at the very same time both a whole
and a part. Person (or individual) represents at the same time the highest
level of the organismic hierarchy and the lowest level of the social hierarchy.
Each system as a whole has its own unique characteristics and dynamics; as
a part it is a component of a higher-level system. The designation "system"
bespeaks the existence of a stable configuration in time and space, a configu-

ration that is maintained not only by the coordination of component parts in

39

SYSTEMS HIERARCHY
(LEVELS OF ORGANIZATION)

BIOSPFHERE

l
SOCIETYf NATION

l
CULTURE - STUBCULTURE

l
COMMTUNITY

l
FAMILY
T
.L
TWO PERSON

l

PERSON
(Experience & Behavior)

T
l
NERVOUS SYSTEM

l
ORGAN S/ORGTAN S SYSTEMS

l
TISSTUES

l
CELLS
T

l.
ORGANFELLES

l
MOLECULES

i
ATOMS
T

l
SUBATOMIC PARTICLES

 

 

 

Figure 3. Hierarchy of Natural Systems [Reproduced with permission from:
"The Clinical Application of the Biopsychosocial Model" by G. L.
Engel,1980, The American Journal of Psychiatgr, m, p. 537.]

40

 

Biosphere
Society - Nation
Culture - Subculture
Community
Family
Two Person
Person
Nervous System
Organ/Organ Systems
Tissue
Cell
Or {anelle

. ole-
cule

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 4. Continuum of Natural Systems
[Reproduced with permission from: "The Clinical Application of

the Biopsychosocial Model" by G. L. Engel, 1980, W
MW .131 P 537]

41
some kind of internal dynamic network but also by the characteristics of the
larger system of which it is a component part. Stable configuration also
implies the existence of boundaries between organized systems across which
material and information flow.

Nothing exists in isolation. Whether a cell or a person, every system is
influenced by the configuration of the systems of which each is a part, that is,
by its environment. More precisely, neither the cell nor the person can be
fully characterized as a dynamic system without characterizing the larger
system(s) (environment) of which it is a part. This is implicit in the labels
used. The designation "red blood cell" identifies directly and by implication
the larger systems without which the red blood cell has no existence. The
term "patient" characterizes an individual in terms of a larger social system.
Identification of the patient by name, age, sex, marital status, occupation,
and residence identifies other systems of which that patient is a component
and which in turn are part of his or her environment. Thus, one may
consider the possibility that the healing of breast cancer can be enhanced by
conjoint imaging with a supportive spouse.

In another article, Engel traces the foundation of his model (Engel,
1992b): Arguing the need for a more fundamental reorientation in scientific
perspectives in order to open the way to holistic approaches more amenable to
scientific inquiry and conceptualization, von Bertalanffy developed general
systems theory. This approach, by treating sets of related events collectively
as systems manifesting functions and properties on the specific level of the
whole, has made possible recognition of isomorphies across different levels of
organization, as molecules, cells, organs, the organism, the person, the
family, the society, or the biosphere. From such isomorphies can be

developed fundamental laws and principles that operate commonly at all

42
levels of organization, as compared to those which are unique for each. Since
systems theory holds that all levels of reorganization are linked to each other
in a hierarchical relationship so that change in one affects change in the
others, its adoption as a scientific approach should do much to mitigate the
holist-reductionist dichotomy and improve communication across scientific
disciplines. For medicine, systems theory provides a conceptual approach
suitable not only for the proposed biopsychosocial concept of disease, but also

for studying disease and medical care as interrelated processes.

Research Objectives

The purpose of this research was to study Six premenopausal women
volunteers who have followed a standard course of treatment for breast
cancer to determine whether a change in blood assays is present following
immunoimagery, and whether this change is greater when she images
conjointly with her husband or when immunoimaging alone. Information
about the woman's treatment course, stage designation at the time of
surgery, the type of surgical procedure(s), the number of lymph nodes
involved, and the presence and extent of metastasis was gathered.

Six blood count measures were obtained for each of the wife
participants: White Blood Cell (WBC) counts, Absolute Lymphocyte (Abs
Lymphs) counts, total T-cell counts (CD3), helper/inducer T-cell counts (CD4),
suppressor/cytotoxic T-cell counts (CD8), and Segmented Neutrophils (Segs),
generally reported as percentage of the WBCS. These measures were selected
owing to their merit in reflecting immune system efficacy.

WBCs are the chief representatives of the immune system; they have
the ability to identify foreign invaders in the body. Lymphocytes (including

B-cells and T-cells) are white blood cells having a variety of functions that

43

circulate in lymph fluid; their numbers are reflected by the Abs Lymphs
counts. T-cells are a type of lymphocyte that are responsible for cellular
immunity. T-cells are derived from bone marrow and stored in the thymus.
There are many different types of T-cells and their total count is represented
by Cluster Designation 3 (CD3). Approximately 90% of CD3 cells are helper/
inducer T-cells (CD4) and suppressor/cytotoxic T-cells (CD8). Helper T-cells
(CD4) cooperatively interact with B-cells. Suppressor T-cells (a component of
CD8) monitor cellular activity and are instrumental in reducing the activity
of parts of the immune system when those parts are no longer needed.
Cytotoxic or killer T-cells (another component of CD8) are T-cells that
migrate to the site of a foreign invader. They attach themselves to the non-
self substance and secrete a chemical that destroys the antigen. Segmented
neutrophils (Segs) are mainly responsible for fighting infections. They
continually circulate in the body, looking for bacteria that do not belong.
Segs are the first line of defense. They react quickly to engulf and digest
foreign matter. Neutrophils comprise 50% - 65% of WBCS.

The total white blood cell count is the enumeration of all nucleated
cells in the sample. The differential white blood cell count gives the
proportions of different cell types that comprise the total number of white
blood cells. The normal ranges for these blood measures for adults are as
follows: WBC--4000-12000; Abs Lymphs--1000-4800; CD3--1120-2580; CD4--
660-1500; CD8-—360—850; and Segs--2500-6500.

Marital adjustment, as measured by the Dyadic Adjustment Scale
(Spanier, 1976), was administered to husband and wife and the scores were
compared with the changes in blood cell numerosity in each condition.
Further, husbands and wives were asked to depict the cancer and the wife's

immune system forces (white blood cells, specifically T-cells) through draw-

44
ings following the imagery. The drawings were scored by two independent
evaluators in accordance with the standardized IMAGE-CA (Achterberg &
Lawlis, 1984), and were related to the post-treatment blood count measure-

ments.

Conceptual and Operational Definitions
Husband's and wife's perceptions of marital adjustment:
Conceptual: The husband's and wife's individual contentment
with the spouse and the marital relationship.
Operational: The husband's and wife's score on the Dyadic

Adjustment Scale.

Husband's and wife's perception of the strength of the cancer and the
strength of the wife's immune system:
Conceptual: The husband's and wife's individual drawn
illustrations and oral descriptions the cancer cells
and the immune system's cancer-fighting cells.

Operational: The husband's and wife's scores on the IMAGE-CA.

Research Assumptions and Conditions

1. Human beings are capable of imaging to some degree.

2. Human mental and physical functions are inter-dependent.

45
3. The human mind, through imagery, is capable of effecting
physiological changes within the body to some degree.

4. Laboratory blood analyses can be demonstrably accurate and

reliable.

5. Certain cellular changes can represent the body's healing processes.

6. Beliefs, attitudes and stress affect the immune system.

7. Breast cancer patients and their husbands are motivated to

participate in guided imagery to the best of their ability.

8. Volunteering to participate in this study is an indication of the

husband's supportiveness for his wife.

Research Questions

Can the imagination exert a positive influence on immunology, and on
cancer? Will conscious manipulation of the immune system through imagery
be related to increased blood counts? Will the strength and vividness of the
images drawn by husband and wife be related to the changes revealed by the
blood analysis? Further, will any salutary effects upon the immune system

increase when a woman in a satisfying marriage images with her husband?

46

Research Hypotheses

Null Hypothesis 1: There will be no difference between the white blood
cell (WBC), absolute lymphocyte (Lymphs), total (CD3),
helper/inducer (CD4), suppressor/killer (CD8) T-cell, and
segmented neutrophil (Segs) counts between the baseline
measure and the subsequent pre- and post-treatment measures.

Working Hypothesis 1 a: There will be differences white blood cell
(WBC), absolute lymphocyte (Lymphs), total (CD3),
helper/inducer (CD4), suppressor/killer (CD8) T-cell, and
segmented neutrophil (Segs) counts in the pre- and post-

treatment measures relative to the baseline measure.

Null Hypothesis 2: There will be no differences between the white
blood cell (WBC), absolute lymphocyte (Lymphs), total (CD3),
helper/inducer (CD4), suppressor/killer (CD8) T-cell, and
segmented neutrophil (Segs) counts between the pre-imagery
blood draws and the post-imagery blood draws.

Working Hypothesis 2a: There will be differences in the white blood
cell (WBC), absolute lymphocyte (Lymphs), total (CD3),
helper/inducer (CD4), suppressor/killer (CD8) T-cell, and
segmented neutrophil (Segs) counts following the solitary and

conjoint imagery sessions relative to the pre-session values.

Null Hypothesis 3: There will be no differences between the changes in
white blood cell (WBC), absolute lymphocyte (Lymphs), total
(CD3), helper/inducer (CD4), suppressor/killer (CD8) T-cell, and

47
segmented neutrophil (Segs) counts measured under the solitary
and conjoint imaging conditions.

Working Hypothesis 3a: There will be differences in the changes in
white blood cell (WBC), absolute lymphocyte (Lymphs), total
(CD3), helper/inducer (CD4), suppressor/killer (CD8) T-cell, and
segmented neutrophil (Segs) counts following the conjoint
imagery condition with respect to those measured under the

solitary imagery condition.

Null Hypothesis 4: There will be no relationship between the strength
and vividness of the wife's imagery (measured by the IMAGE-
CA) and her post-imagery white blood cell (WBC), absolute
lymphocyte (Lymphs), total (CD3), helper/inducer (CD4),
suppressor/killer (CD8) T-cell, and segmented neutrophil (Segs)
count changes.

Working Hypothesis 4a: The stronger and more vivid the imagery
(higher IMAGE-CA score), the greater the change in white blood
cell (WBC), absolute lymphocyte (Lymphs), total (CD3),
helper/inducer (CD4), suppressor/killer (CD8) T-cell, and
segmented neutrophil (Segs) counts following the solitary and

conjoint imagery sessions.

Null Hypothesis 5: There will be no relationship between DAS scores
and the differences in the white blood cell (WBC), absolute
lymphocyte (Lymphs), total (CD3), helper/inducer (CD4),
suppressor/killer (CD8) T-cell, and segmented neutrophil (Segs)

counts between the solitary and conjoint imagery conditions.

48
Working Hypothesis 5a: The higher the wife’s and husband's DAS
scores, the greater the change in the white blood cell (WBC),
absolute lymphocyte (Lymphs), total (CD3), helper/inducer
(CD4), suppressor/killer (CD8) T-cell, and segmented neutrophil

(Segs) counts between the solitary and conjoint conditions.

Research Design

An exploratory double-blind quasi-experimental design was employed
to achieve the objectives of this study. The unit of analysis is comparative
white blood counts, T-cell counts, and neutrophil counts on blood drawn from
the breast cancer patient before the training session and prior to and follow-
ing each of the two guided imagery sessions. Ethical and financial considera-
tions required this investigation to be conducted in a natural setting with a
convenience sample (premenopausal married women with breast cancer who
had completed a standard course of surgical and systemic treatment, and who
along with their husbands chose to participate in this study). The women
were randomly assigned to two groups (image alone first; image with
husband first). Subjects were recruited as volunteers from physicians and
nurses in the Lansing/Grand Rapids area who specialize in the treatment of
women with breast cancer. Physicians did not refer any patient for whom
five blood draws in fifteen days was contraindicated. The women and their
husbands volunteered for this study after learning about the imagery compo-
nent, and the commitment to three sessions spaced one week apart, with
blood draws at each session. No patient or insurance company was asked for

reimbursement for the blood chemistry connected with this study. Prior to

49
the training session, both husband and wife completed the Dyadic
Adjustment Scale (Spanier, 1976, Appendix A).

There was a sample group of six women who imaged alone and with
their husbands. The subjects served as their own control, with three of the
women first imaging with their husbands, and the remaining three imaging
alone first. A baseline white blood count, T-Cell count (CD3, CD4, CD8), and
neutrophil count was established prior to educating the couples about the
immune system and guiding them to focus on blood cell increments, espe-
cially cytotoxic T-cells. It is possible that learning about the immune system
and how it fights disease could alter blood cell scores even prior to the treat-
ment. Further, these subjects were assumed to be highly motivated as a
result of a potentially life-threatening diagnosis and they may have begun
thinking about (i.e., rehearsing) the imagery process or immune system func-
tion for a period of time prior to the pre-treatment blood draws, which could
affect white blood cell numerosity in the pre-treatment blood samples.
Therefore, recruitment of couples was accomplished by presenting in only
general terms the goal of promoting well-being through the use of imagery,
and promising the volunteers a closure meeting following completion of the
project, when the hypotheses and results could be more fully described.

For each subject, all blood samples were drawn within ninety minutes
of the same time of day, to control for ultradian effects. The blood samples
consisted of 12.5 cm3 of blood drawn from the patient's arm (contralateral to
the mastectomized side) into pre-sealed vacuum tubes. Venipuncture was
performed by a registered nurse, and if the nurse predicted any difficulty in
obtaining a successful draw, another experienced nurse from the IV team
performed the draw. All lymphocyte assays were carried out on fresh cells

within twenty-four hours after the blood was drawn. A comprehensive proce-

50
dural description for the Complete Blood Count (CBC) and the T-cell
Absolutes (CD3, CD4, CD8) is contained in Appendix B.

During the first session, the baseline blood draw preceded each
woman's completion of a medical history form and demographic question-
naire. Both husband and wife then completed the Dyadic Adjustment Scale
and were instructed not to communicate with one another during the admin-
istration of the measure. Immune system education consisted of the couple
watching a fifteen minute film (Bioimagery, 1993) about immune system
function (Appendix C). The couple was assured that it would not be
necessary for them to remember in detail the information provided by the
film, but rather that they obtain a general overview of how the immune
system functions, paying particular attention to the information about T-
cells. After viewing the video, each couple had the opportunity to share their
reactions about the experience for five to ten minutes and the video was
shown a second time.

In the second and third sessions the imagery exercise began with a
tape recorded (the experimenter's voice with background music) induction of
deep relaxation (6 minutes). The focusing phase (30 minutes) began with the
suggestion that the subjects place themselves in a safe and relaxed setting
where all things are possible. They were then given general guidelines
regarding the immune system and health-promoting cellular processes.
Subjects were asked to get a sense of the T-cells and their activities of multi-
plication, surveillance, and destruction of any malignant cells in the woman's
body. No specific images were suggested, although the participants were
encouraged to introduce an element of playfulness in their images. A six
minute reversal of the deep relaxation concluded the tape recording. (The

immunoimagery audiotape script is reproduced in Appendix D.)

51

For thirty minutes following the guided imagery, and prior to the veni-
puncture, the subjects were asked to draw their imagery experience on white
11" x 14" drawing paper. It was suggested that these drawings include the
participant's image of any malignant cells in the body, the woman's white
blood cells including the T-cells, the woman’s external resources including
medical treatment, and how the white blood cells including T-cells interacted
with the malignant cells. These drawings were scored according to the stan-
dardized IMAGE-CA (Appendix E).

Since it is important to learn about any factors that might affect the
female participant's immune system, the woman was asked to document all
medications taken during the month prior to the first session (thereafter,
medications used during the weekly intervals), and to describe medical
treatment, any acute illness, and interpersonal or other changes that had
occurred since the previous interview.

A flow chart summary of the research protocol is provided in Table 1.

Instrumentation and Measurement Procedures

Marital contentment and satisfaction were measured by asking each
breast cancer patient who met the selection qualifications and her husband,
who volunteered for this study, to complete the Dyadic Adjustment Scale
(DAS) [Appendix A]. The DAS is a 32 item self-report Likert-style question-
naire instrument with 5-, 6-, and 7-point response formats ranging from
either always agree to always disagree or all the time to never (Spanier, 1976).
Of the more than 1000 reported studies that utilized the DAS, 90% have
involved married couples (Touliatos, Perlmutter, & Straus, 1990). Factor
analysis identified four areas: dyadic satisfaction, dyadic cohesion, dyadic

consensus, and affectional expression (Spanier, 1976). Cronbach's alpha = .96

52

Table 1. Research Protocol

Recruitment ef Subjects

 

 

l

Seseion One
blood draw A

 

 

 

 

 

contact local breast cancer surgeons, oncologists, radiation-oncologists,
support group leaders, Breslin Cancer Center, and MSU Breast Clinic

screen volunteers for suitability and explain expectations of participants;
randomly assign subjects to the two groups (solitary, conjoint)

background interview; informed consent; release of medical information

 

 

 

woman's completion of medical history form and demographic questionnaire
completion of Dyadic Adjustment Scale by wife and husband

immune system educational film

discussion of reaction to film

i

l

l

l

' introduction of film

1 second viewing of film

i

 

 

_ 3 eggplee where wife images alone ’
(couples 1, 3, 5)

 

 

3 ceuples where husband & wife
image together (couples 2, 4, 6)

 

 

i l

l

 

 

Mes Husbands

complete short weekly watch action video
medical update in another room
blood draw B "
guided imagery tape
draw images (30 min)
blood draw C
structured interview
(IMAGE-CA)

 

 

 

 

Wives and Hdsbands

wife completes short
weekly medical update
blood draw B
guided imagery tape
draw images (30 min)
blood draw C
structured interview
(IMAGE-CA)

 

 

 

 

 

 

 

53

Table 1
Research Protocol
(continued)

I
i

ll .Sflifll—TIJLBQ ll
l

3 eeaplee where wife images alone I 3 couples where husband & wife

 

 

(couples 2, 4, 6) image together (odd no. couples)
i i l

 

 

 

 

 

 

 

 

 

 

Wives Maude MW
complete short weekly watch action video wife completes short
medical update in another room weekly medical update
blood draw D blood draw D
guided imagery tape guided imagery tape
draw images (30 min) draw images (30 min)
blood draw E blood draw E
structured interview structured interview
(IMAGE-CA) (IMAGE-CA)

 

 

 

 

 

     

   

f—_‘

' Informational Session
L- Review the project and discussion of the results with the volunteers

  

54

for the entire scale, and ranges from .73 to .94 for the subscales (Sabatelli,
1988). Sabatelli (1988) reports that the validity of the scale is supported by
the consensus of judges on the relevance of the scale's content and that the
scale has been successfully used retrospectively when respondents were
asked to recall the last months of their marriages. In addition, there is a
high correlation (r = .88) between the DAS and Locke-Wallace Short Marital
Adjustment Test (LWMAT) (Sabatelli, 1988).

Complete procedural descriptions of the CBC and T-cell Absolute blood
assays have been provided by the Sparrow Hospital Laboratory where the
blood was analyzed within twenty-four hours after each draw. They are
reproduced in Appendix B.

Two modes of communication were used in completing the IMAGE-CA:
a personalized drawing of the disease and internal and external resources,
and a structured interview. The reliability of the IMAGE-CA has been
assessed by both interdimensional correlation and interrater correlation. The
correlations for the dimensions applicable to this project range from .27
(vividness of the cancer cell) to .93 (overall imagery strength). Interrater
reliability correlations between two independent raters were all statistically
significant and range from .60 (strength of cancer cells) to .87 (symbolism) for
the dimensions applicable to this study. The measurements also were
analyzed to determine whether the raters were in agreement about absolute
levels, and one hundred percent agreement was found (Achterberg & Lawlis,
1984).

The combined dimensions of the IMAGE-CA have been used to predict
current health status and to project health status in two months. Health
status was categorized as death, evidence of new tumor growth and degenera-

tive disease, stabilized condition, evidence of reduction of existing tumor(s)

55
and positive process, evidence of complete absence of tumor(s) or disease.
Ninety-three percent prediction was obtained for favorable prognosis and one
hundred percent prediction was obtained for unfavorable prognosis
(Achterberg & Lawlis, 1984). Reliability and validity coefficients remained
similar for a sample of twenty-one patients (racially mixed group) from low
income and educational levels.

In this study, after the woman or couple was guided through relaxation
and imagery, focusing on the disease and the white blood cells (T-cells in
particular) and external resources including medical treatment, the subject(s)
was(were) asked to draw the images with crayons (64 color assortment). A
structured interview followed to clarify and objectify the meanings underly-
ing the drawings. The drawings and interviews were scored on the following
dimensions using a 5-point scale: vividness of the cancer cell, activity of the
cancer cell, strength of the cancer cell, vividness of the white blood cell (T-
cell), activity of the white blood cell (T-cell), relative comparison of numbers
of cancer cells to white blood cells (T-cells), relative comparison of size of
cancer cells to white blood cells (T-cells), strength of the white blood cells (T-
cells), concreteness versus symbolism, and overall strength of the imagery
(Achterberg & Lawlis, 1984). The interviews were both video and audio tape
recorded and evaluated along with the drawings by two independent, trained

raters and their ratings are compared and reported.

Sampling Methods and Data Collection Procedures

The men and women who agreed to participate were studied over a 15
day period of time. The woman had been diagnosed with premenopausal
breast cancer, had followed a standard course of surgical and systemic treat-

ment, and was judged by her physician capable of sustaining five blood draws

56

over a fifteen day period without compromising her health status. The
couples included in this study were all married and living together. Each
couple member was able to read, write, and speak English. Each respondent
gave written and verbal informed consent [Appendix F], and made an initial
commitment to participate in this study consisting of three one- to two-hour
sessions spaced one week apart, accompanied by blood draws. Each woman
had to obtain her physician's written approval in order to participate in this
study. Further, it was made clear to each participant that this study was a
preliminary investigation that in no way condoned the use of imagery as a
substitute for conventional medical treatment, nor promised any salutary
effects as a result of its use [Appendix G].

The sample group was collected from those couples meeting the above
selection criteria who agreed to participate in this investigation and who
lived within a seventy-mile radius of Lansing, Michigan. Permission for the
study was requested and granted from the Michigan State University
Committee On Research Involving Human Subjects (UCRIHS) [Appendix H].
Physicians specializing in the treatment of women with breast cancer, oncol-
ogy or radiological oncology and nurses who conduct breast cancer support
groups were asked to identify a woman's potential suitability for inclusion in
the study. Information about the project and the researcher’s name and tele-
phone number was disseminated to potential subjects by mail or at support
groups. If a couple was interested, they contacted the researcher.

Consent of the couple was obtained by personal interview after verbal
interest has been indicated and satisfaction of selection criteria was ascer-
tained. Many people have either heard or read about imagery and its use
with cancer patients. Some of the participants believed in its possible efficacy

and volunteered to learn more about it; others were more circumspect, yet

57

open to the possibility of its helpfulness. After hearing a general description
of the project and giving verbal and written informed consent [Appendix F],
the couple was scheduled for three successive weekly sessions, to occur at the
same time each week. (T-Cell Absolutes are less variable when the blood is
drawn at similar times of the day.) If participation by otherwise interested
and suitable volunteers was precluded by transportation difficulties, it was
determined that rides would be offered to and from the research site. All
volunteers had their own transportation. The forms used to obtain physician
consent, to establish demographic and medical data, and to collect recent
information about the participants, are reproduced in Appendix I.

At the first session the woman's blood was drawn; the couple filled out
the DAS; they watched a 15 minute educational video (Bioimagery, 1993),
discussed their reactions to the video and then watched it a second time. A
phone call was made the day before the second session to remind the couple of
the importance of being on time for the successive blood draw. Husband and
wife each had a couch to rest on during the imagery sessions; the pre-and
post-treatment blood draws were conducted while the woman sat on a couch.
Following venipuncture, the second session for half the couples consisted of
imaging the woman's cancer, then imaging the body's response to the cancer,
in particular the T-cell response, and to imagine the woman’s external
resources (family, friends, prayer, medical treatment, community) guided by
the researcher's voice on a tape cassette; of drawing the imagery experience
for up to thirty minutes post imagery; and completing the post-treatment
blood draw at thirty minutes post imagery. Only then did the couple move
from the couches they occupied during the imagery and drawing process. (On

several occasions a participant used the restroom prior to venipuncture.)

58

Again, a prior day's phone call reminder stressed the importance of
being on time for the fourth blood draw, which began the third session. The
procedure for the same three couples consisted of the identical process as
described in Session Two, but this time for the woman only. The husband
was in a separate room watching a distracting, action-filled video in an
attempt to control for supportive husbands who may be thinking about their
wife's participation in the study and her immune system response, thereby
reducing a potential confounding variable. The husbands were not asked not
to image their wife's immune response, because the suggestion may actually
increase the impulse; instead, the action-filled video was provided in an effort
to circumvent his empathic focus on bolstering her T-cells. The procedures
described for the second and third sessions were reversed for the other half of
the volunteer group.

This study utilized a purposive nonprobability sample, and its findings
describe six marital dyads in which the husband's participation was support-
ive of the wife and in which the female has been diagnosed with
premenopausal breast cancer and followed a standard course of surgical and

systemic treatments.

Data Analysis

This was a pilot investigation, circumscribed by the limited funding
procurable for the expensive blood analyses; moreover, because the female
participants were required to satisfy certain medical criteria and were asked
to submit to five blood draws, there was great difficulty in recruiting volun-
teer subjects. Therefore, this was more of a preliminary study than one from

which the findings can be statistically generalized. If the results suggested

59
an imagery effect or a solitary/conjoint imagery effect, then the expense of a
larger scale investigation could be justified.

This investigation involved a single subject design, where the results
from individual subjects were examined, and each subject served as her own
control. The use of single subject experimental design is well established in
clinical research and the design efficacy and legitimacy is well documented
(Barlow & Hersen, 1984; Kazdin, 1982; Kratochwill, 1978). The results for
each subject are displayed in the following chapter, in tables that list the pre-
and post-treatment blood analyses, imagery ratings, and marital satisfaction
scores.

Logistic regression, employing several explanatory variables
(solitary/conjoint imagery, imagery sequence, imagery ratings, marital satis-
faction and adjustment) was utilized to characterize the dependent variable
(changes in blood assays). The statistical method of logistic regression is
becoming more frequently used in the health research literature because it
can treat a binary response variable (e.g., success or failure of a given treat-
ment, coded as 1 or 0) as a function of a mixture of continuous or nominal
independent variables (Hirsch & Riegelman, 1991). In the conventional,
multiple linear regression model, a continuous, normally distributed response
variable (Y) is fitted to a linear combination of several predictor variables (X):
Y = b0 + b1*X1 + b2*X2 + + bk*Xk.

In the linear logistic model it is assumed that for each set of values of
the X variables there is a probability p that success occurs. The odds of
success are therefore [p/(1-p)], and can range from zero to positive infinity. In
logistic regression the natural logarithm of the odds ratio is taken as the
independent variable, so that the range is from minus to plus infinity, with

zero reflecting equal probability of success or failure. The “logit transforma-

60

tion” of the probability is formed, such that Y in the above equation is
replaced by ln[p/(l-p)]. The bj constants are then fitted by multiple linear
regression. [Several good introductions to logistic regression are available.
See for example Hosmer & Lemeshow, and Kleinbaum (Hosmer &
Lemeshow, 1989; Kleinbaum, 1994)].

Logistic regression can be applied successfully to sparse and imbal-
anced data sets. Nonetheless, despite the use of a multivariate model, the
external validity of this study is threatened as the result of the small sample

size, and generalizability is limited.

Limitations of the Research

Marital satisfaction and marital adjustment were assessed retrospec-
tively through husband's and wife's own perceptions. There may be biases
resulting from the naturalistic selection of respondents, the small sample size
and the absence of separate control and placebo groups (which were elimi-
nated due to the difficulty in recruiting subjects and the costly blood chem-
istry analyses). The sample of couples all resided within seventy miles of
Lansing, Michigan, which may further limit the generalizability of the

research results.

CHAPTER IV
RESULTS AND DISCUSSION

The protocol was designed to examine whether imaging alone or as a
couple affects blood cell measures, specifically changes in white blood cell
(WBC), lymphocyte (Lymphs), T-cell (CD3, CD4, CD8), and segmented
neutrophil (Segs) numerosity. The blood count changes were related to
raters’ assessments of each subject’s imagery, and the couples’ marital satis-
faction. The study is limited by the small sample Size and the impossibility of
controlling all confounding variables. These factors restrict the statistical

methods that can be employed.

Demographic Information

Six volunteer breast cancer patients who met the prescribed criteria,
and their husbands, participated in this study. All of the volunteers were
Caucasian and of middle socioeconomic status. All of the women had experi-
enced premenopausal onset, at a mean age of 44.3 years (range: 33 years, 4
months to 56 years, 1 month). Their mean age at the time the study was
conducted was 47.5 years (range: 34 years, 6 months to 59 years, 1 month).
All six were surgically treated; five had unilateral mastectomies and one had
a bilateral mastectomy. Two had surgical breast reconstruction as well. All
participants received chemotherapy as part of their treatment protocols,
ranging from four to eleven treatments. One was treated with radiation, as
well. Three of the participants were receiving hormonal therapy in the form

of Tamoxifen. The subjects had no, one or two lymph nodes that were malig-

61

62

nant; none had metastasis. All participants had Stage 1 or 2 cancer classifi-
cation. Three of the participants were estrogen receptor positive, one both
estrogen and progesterone receptor positive, and two were estrogen receptor
negative. Three reported consulting a mental health professional in relation
to the cancer diagnosis (one of the three was currently involved in marital
therapy) and two reported being in cancer support groups. In addition to
breast cancer, one of the participants had heart disease treated by quadruple
bypass surgery, fibromyalgia, systemic lupus, and a thyroid condition treated
by oral medication. Four of the participants volunteered that they had tried
alternative treatments including massage, imagery, polarity therapy, reflex-
ology, prayer, and exercise, along with standard medical treatment.

It was not possible to control for diseases, stressors and pharmaceuti-
cals that could affect the participants’ immune systems. In order to identify
such potential confounding issues, each subject was asked to fill out a ques-
tionnaire about illness, drugs, and interpersonal stressors before each
session. During the course of the study each participant noted information
that could have impacted her immune system. The following items were
reported by the participants in the course of their 15-day observation periods:
colds, allergies, fusarian, depression, sore throat, sick and injured pets, body
aches, abnormal digestion, nausea, runny nose, possible cancer recurrence,
serious illness of family members, family members involved in a car accident,
marital problems, death of a close family member, and the attempted suicide
of a close family member. Although three of the participants were taking
twelve prescriptive drugs, none of these were known to be immunosuppres-

sants. All the volunteers denied ingesting any non-prescribed drugs.

63

Laboratory Results and Primary Data

The results of the laboratory analyses of the blood drawn from the
volunteer subjects at the designated times (Draws A through E), as well as
the imagery ratings (IMAGE-CA Scores) and the couples’ Dyadic Adjustment
Scale scores, are presented in Tables 2 - 9. Photocopied reductions of the
wives’ drawings following each imagery session are collected in Appendix J.
It should be noted that the segmented neutrophils were reported by the
laboratory in percentages of the white blood cell count. The percentages were
transformed into the absolute numbers shown in Table 7 by multiplying the
reported white blood counts by the reported percentage segmented
neutrophils. Also, owing to accidental hemolysis of the sample in one
instance, the third session for Couple Four had to be repeated, two weeks
after the original date. Data for the later, repeated session are entered as

Draw D and Draw E, in lieu of the original values.

Interpretation

For each blood variable analyzed, there are three distinct trend possi-
bilities in each the Sequence condition (first session/second session) and in
the Alone/Together condition. Blood counts could decrease in numerosity
following the guided imagery, blood counts could remain the same (within the
measurement error) with respect to the pre-imagery values, or blood counts
could increase in numerosity following the guided imagery. Figure 5

illustrates the trend possibilities.

Table 2. White blood cell counts (cells/mL) relative to sequence and to

 

 

64

 

 

 

 

 

solitary vs. conjoint imagery.

Sequence Baseline Session One Session Two

Draw A Draw B Draw C Draw D Draw E
Couple 1 4200 5300 4700 4400 4000
Couple 2 4700 47 00 4200 4100 3900
Couple 3 5700 6100 4800 5200 5000
Couple 4 4900 4500 4300 5200 4800
Couple 5 8100 7500 6800 6800 7400
Couple 6 3900 4300 3600 4100 3200

Alone/ Baseline Wife Alone Couple Together

Together

Draw A Pre Post Pre Post
Couple 1 4200 5300 4700 4400 4000
Couple 2 47 00 4100 3900 4700 4200
Couple 3 5700 6100 4800 5200 5000
Couple 4 4900 5200 4800 4500 4300
Couple 5 8100 7500 6800 6800 7400

Couple 6 3900 4100 3200 4300 3600

 

 

65

Table 3. Absolute lymphocyte counts (cells/mL) relative to sequence and to
solitary vs. conjoint imagery.

 

 

Sequence Baseline Session One Session Two
Draw A Draw B Draw C Draw D Draw E

Couple 1 1344 1330 1344 1505 1468
Couple 2 1800 1598 1420 1304 1119
Couple 3 1693 2001 1723 1586 1520
Couple 4 715 891 937 1076 1032
Couple 5 2066 2018 1863 1815 2131
Couple 6 998 1058 896 1066 826

 

 

 

Alone/ Baseline Wife Alone Couple Together
Together
Draw A Pre Post Pre Post
Couple 1 1344 1330 1344 1505 1468
Couple 2 1800 1304 1119 1598 1420
Couple 3 1693 2001 1723 1586 1520
Couple 4 715 1076 1032 891 937
Couple 5 2066 2018 1863 1815 2131

 

 

66

Table 4. CD3 counts (cells/mL) relative to sequence and to solitary vs.
conjoint imagery.

 

 

 

 

Sequence Baseline Session One Session Two

Draw A Draw B Draw C Draw D Draw E
Couple 1 766 665 659 856 749
Couple 2 1386 1230 1093 1082 884
Couple 3 1185 1401 1206 1142 1125
Couple 4 472 499 619 427 699
Couple 5 1756 1634 1490 1470 1716
Couple 6 699 762 609 800 595

Alone/ Baseline Wife Alone Couple Together

Together

Draw A Pre Post Pre Post
Couple 1 766 665 659 856 749
Couple 2 1386 1082 884 1230 1093
Couple 3 1185 1401 1206 1142 1125

Couple 4 47 2 427 699 499 619

 

 

67

Table 5. CD4 counts (cells/mL) relative to sequence and to solitary vs.
conjoint imagery.

 

 

 

 

Sequence Baseline Session One Session Two

Draw A Draw B Draw C Draw D Draw E
Couple 1 430 372 363 452 440
Couple 2 648 623 540 535 425
Couple 3 609 720 638 571 562
Couple 4 293 267 328 430 402
Couple 5 950 908 820 835 895
Couple 6 529 561 448 597 438

Alone/ Baseline Wife Alone Couple Together

Together

Draw A Pre Post Pre Post
Couple 1 430 372 363 452 440
Couple 2 648 535 425 623 540
Couple 3 609 720 638 571 562
Couple 4 293 430 402 267 328
Couple 5 950 908 820 835 895

 

 

68

Table 6. CD8 counts (cells/mL) relative to sequence and to solitary vs.
conjoint imagery.

 

 

 

 

 

Sequence Baseline Session One Session Two
Draw A Draw B Draw C Draw D Draw E
Couple 1 323 279 282 376 294
Couple 2 720 623 511 548 436
Couple 3 559 660 534 476 441
Couple 4 172 187 234 269 268
Couple 5 744 706 689 635 789
Couple 6 170 180 152 181 149
Alone/ Baseline Wife Alone Couple Together
Together
Draw A Pre Post Pre Post
Couple 1 323 279 282 376 294
Couple 2 720 548 436 623 511
Couple 3 559 660 534 476 441
Couple 4 172 269 268 187 234
Couple 5 744 706 689 635 7 89
fl Cule6- _» . 170 __ _ j 181 149 _ 180 _ _ f 152

 

   

69

Table 7. Segmented neutrophil counts (cells/mL) relative to sequence and to
solitary vs. conjoint imagery.

 

 

Sequence Baseline Session One Session Two
Draw A Draw B Draw C Draw D Draw E
Couple 1 2066.4 3190.6 2730.7 2019.6 1820.0
Couple 2 2411.1 2594.4 2419.2 2427.2 2390.7
Couple 3 3243.3 3172.0 2553.6 2844.4 2770.5
Couple 4 3895.5 3150.0 2906.8 3619.2 3336.0
Couple 5 5281.5 4815.0 4256.8 4256.8 4417.8

Couple 6 2457.0 2691.8 2217.6 2546.1 2009.6

 

 

 

Alone/ Baseline Wife Alone Couple Together
Together
Draw A Pre Post Pre Post
Couple 1 2066.4 3190.6 2730.7 2019.6 1820.0
Couple 2 2411.1 2427.2 2390.7 2594.4 2419.2
Couple 3 3243.3 3172.0 2553.6 2844.4 2770.5
Couple 4 3895.5 3619.2 3336.0 3150.0 2906.8
Couple 5 5281.5 4815.0 4256.8 4256.8 4417 .8

 

 

2217.6

  

70

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+Possibiiity 1
80 1 +Possibility 2

 

 

Blood Count

 

 

50 Possibility 3
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20

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LLI
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Figure 5. Trend Possibilities

The data were analyzed by subject and in the aggregate, for each of the
blood measures. According to the Sparrow Hospital Laboratory Director, the
assayed blood counts should be accurate within :i: 10%. Changes in the
average value of a particular pre- or post-imagery blood count, over time and
whether imagery was solitary or conjoint, were generally small. As
illustrated in Figures 6 and 7 for trends in the pre- and post-imagery
segmented neutrophil counts, the averages mask larger, but sometimes
opposite, changes measured for the specific subjects. Therefore, in the final

analysis the data for each subject were treated separately.

73

 

    

 

 

 

Segmented Neutrophils
6000
_._-_.5si--—- Couple 1
5000 .- —'O—- Couple 2
—'O— Couple 3
4000 - —fi-—- Couple 4
+ Couple 5
3000 J __ + Couple 6
A , + Average
2000 -.—7 f
< m D

Pie-Imagery Blood Draws

Figure 6. Illustration of trends in individual data being obscured by
averaging

4500

74

Segmented N eutrophils

 

4000 -

3500 -

3000 -l

2500 -

2000 -

 

1500

/.

 

 

I I
U 9.3
3
E E

Post-Imaging Blood Draws

Couple 1
Couple 2
Couple 3
Couple 4
Couple 5
Couple 6

Average

Figure 7. Example of how opposing trends are obscured by averaging

75

For those aspects measured, the span in blood count numerosities
among the subjects range from more than a factor of two to almost a factor of
five; see Tables 2 - 7. The ranges for the IMAGE-CA and DAS scores are
much smaller; see Tables 8 and 9.

The focus of this investigation is the change in blood cell counts that
occurs when a subject images alone or with her husband. These changes are
illustrated for each couple in Figures 8 - 13. It should be noted that the blood
assay changes are calculated by subtracting the post-imagery blood count
from the pre-imagery value. Thus, a positive number reflects a reduction in
blood count numerosity over the course of the imagery session. Figures 8 - 13
also include the wife’s IMAGE-CA scores. For a given session, the IMAGE-CA
score plotted is the average of the values determined by the two independent
raters. The values are reported in Table 10.

Table 10. Interrater average IMAGE-CA scores for the wives.

 

Couple 1 2 3 4 5 6
Alone 201 202 200 150.5 172 213.5
Tgether 153 197.5 107.5 148.5 135 231

 

 

76

 

COUPLE 1
IMAGE CA & A Blood Counts
soo
soo - —I:I— Image CA
-—-<>-— A WBC
-—O-- A Abs Lymphs
4oo -

—A— A CD3
—33— A CD4
200 -
+ A CD8

A Se gs

l

 

 

-200

 

Cl Alone ‘
C1 Together ‘

Condition

Figure 8. IMAGE-CA and blood count changes for Couple 1

77

COUPLE 2
IMAGE CA & A Blood Counts

600

 

500 '-
+ Image CA
"*0“— A WBC

400 -'
—0—' A Abs Lymphs
—+— A CD3

300 -'
+ A CD4

200 _ _._— A CD8

 

 

+ ASegs

 

100-

ll.

 

 

 

(‘2 Alone "
C2 Together ‘

Condition

Figure 9. IMAGE-CA and blood count changes for Couple 2

78

 

 

 

 

COUPLE 3
IMAGE CA & A Blood Counts
1500
+ Image CA
-—<>—-—- A WBC
1000 -
—"0'— A Abs Lymphs
—fi'—— A CD3
\ —-EB—- A CD4
soo - + A CD8
"—9— A Segs
0 I” 1;
8 2:3
E 3’»
a i3
Condition 8

Figure 10. IMAGE-CA and blood count changes for Couple 3

79

COUPLE 4
IMAGE CA & A Blood Counts

 

 

 

 

 

500
400 J
+ Image CA
9 —'0— A Abs Lymphs
200 -l \>
——A-— A CD3
[3 {I
100 -l + A CD4
+ A CD8
0 —
—9‘— A Segs
‘nj
-100 -
-200 I I
<7- '9‘...
a i3
<1
U

Condition

Figure 11. IMAGE-CA and blood count changes for Couple 4

80

COUPLE 5
IMAGE CA & A Blood Counts

 

1000

0) —'D— ImageCA

\

\__‘
500 d \ —<>-—- A WBC
\ —‘O— A Abs Lymphs

0d —¢-—- ACD3
+ ACD4

\o ——6— ACD8

—9— ASegs

 

-500 -l

 

 

 

-lOOO

C5 Alone ‘
C5 Together "

Condition

Figure 12. IMAGE-CA and blood count changes for Couple 5

1000

81

COUPLE 6

IMAGE CA & A Blood Counts

 

750 -I

500 -

250-

 

 

 

 

 

C6 Alone ‘ V
C6 Together " L

Cmflfimn

hmmeCA
AWBC
AAmmhmmm;
ACD3

ACD4
ACD8

A&ms

Figure 13. IMAGE-CA and blood count changes for Couple 6

82

As illustrated by the preceding figures, the changes in the wife’s blood
counts for Couples 3, 4, 5, and 6 decreased following the conjoint imagery
sessions, with respect to the changes when she imaged alone; the differences
are most dramatic for the White Blood Cell counts. For Couples 3 and 6 the
change in numerosity was smaller when the wife imaged with her husband.
For Couple 4 the absolute lymphocyte counts and the T-Cell absolutes
improved (post-imagery values larger than pre-imagery values) following
conjoint imagery. The Couple 4 wife indicated on the DAS (Score = 84) and
verbally that she was dissatisfied with the marital relationship, and the
couple was receiving weekly marital therapy at the time of this investigation.
Following the conjoint imagery session and the wife’s blood draw, the
IMAGE-CA interview was conducted consistent with the research protocol.
During his retrospective of the imagery experience, the husband described
how supportive he had felt during his wife’s cancer treatment process. His
wife seemed to be witnessing his deep level of caring for the first time. This
interaction occurred after the first imagery session, and the couple referred
back to that debriefing during the course of their participation. It should be
noted that the wife’s blood was drawn prior to this discussion. Nonetheless,
the Couple 4 wife was one of only two participants whose blood assays
increased in the conjoint condition.

Each blood assay increased for Couple 5 in the conjoint condition.
Thus one might have expected either very high IMAGE-CA or DAS scores for
one or both couple members. However, the wife’s IMAGE-CA score dropped 2
stens in the conjoint condition, and the husband’s IMAGE-CA scores were
disparate between the raters. Couple 5’s DAS scores were not exceptional:
100 (husband) and 115 (wife). Further, the wife reported a number of serious

health issues to be present in addition to the breast cancer (heart disease

83
requiring quadruple bypass surgery, systemic lupus, fibromyalgia and a
thyroid condition) which could substantially impact her immune system; yet
all of her baseline blood assays were in the normal range. The Couple 5 wife
reported having a very large support network of friends, family, God, church,
and professionals. This is consistent with Cohen’s recent finding that more
diverse social networks were associated with greater resistance to upper
respiratory illness (Cohen, et al., 1997).

For Couple 1 the WBCS and Segs decreased less in the together condi-
tion, whereas the Lymphs and CD 3 and 8 decreased more in the together
condition. The husband of Couple 1 was physically present during the
imagery session, but denied listening to the tape or participating in the
imagery experience. He reported that instead he had stared at the ceiling
and assessed the quality of the paint job. (He was a professional painter.)

For Couple 2 the WBCS and Segs decreased more in the conjoint condi-
tion, whereas the Lymphs and T-cell measures decreased less or stayed the
same when the couple imaged together. The wife reported on the day of
conjoint imagery that she was experiencing much anger for her husband and
the marital relationship during the month prior to participating in the study.

Since each subject served as her own control, the blood assay changes
depicted in Figures 8 - 13 must be viewed with the knowledge that session
order, and hence a possible learning effect, has not been considered. A mean-
ingful statistical analysis must incorporate the order of the sessions (alone
first vs. together first) when the effect of imaging alone or as a couple is
evaluated. This problem is treated in detail in the section on statistical
analysis.

Does guided imagery help effect an increase in blood count numerosity,

as measured 30 minutes after the imagery session? Changes in six blood

84

measures, over two sessions, were obtained for each of six subjects. In only
eight of 72 instances did the number increase more than 10%; in slightly
more than half of the cases it decreased by more than 10%. Similar results
have been reported by several other investigators (Fawzy, Fawzy, Hyun,
Elashoff, Guthrie, Fahey, & Morton, 1993; Fawzy, et al., 1990; Gruber, et al.,
1988; Gruber, et al., 1993; Hall, et al., 1992; Rider & Achterberg, 1989;
Zachariae, et al., 1990)

Apparently, guided imagery and other relaxation techniques generally
reduce T-cell numerosity and activity on a short time scale. In two studies,
however, efficacious effects of imagery on T-cell activity have been measured
over a period of several months to 18 months (Gruber et al., 1988; Gruber et
al., 1993). In several other studies, group therapy and psychiatric interven-
tion effects required 20 months to develop (Fawzy et al., 1993; Fawzy et al.,
1990; Spiegel et al., 1989) In view of the published information, it is possible
that long-term effects of behavioral interventions such as biofeedback, hypno-
sis, guided imagery or group therapy may be cumulative.

The results of this investigation, which spanned 15 days for all partici-
pants (except Couple 4, for whom the experimental phase spanned 29 days),
support the observation that T-cells do not increase over the immediate
course of an imagery session or over durations as short as 15 days. For each
of the six blood variables measured, and for both sessions, the average post-
imagery numerosities were lower than the average pre-imagery values.

Gruber et a1. (1993) report that both muscle tension and autonomic
activity reductions began within three weeks after initiation of relaxation and
biofeedback training. These psychophysiological reductions occurred prior to
immune system changes, but the actual cause of the immune system changes

was not clear. Over 18 months, the authors found Significant correlations

85
between indices of the immune system and behavioral interventions, leading
them to conclude that the intervention might have the potential to modulate
the immune system either directly or indirectly.

Zachariae et a1. (1994) conjecture that the decreases in lymphocyte and
neutrophil counts after guided imagery “could indicate that brief changes in
cellular immune function may be related to changes in trafficking of leuko-
cytes, which in turn may be influenced by autonomous nervous system activ-
ity.” They suggest the possibility that other mediators such as norepi-
nephrine or opioid peptides could underlie the observed immune function
changes.

As noted earlier, Glaser et al. (1992) found that both stress and social
support were related to medical students’ abilities to produce an antibody
response to the Hepatitis B vaccine. These authors report that convergent
data from both human and animal studies support the idea that stress may
delay the development of an immune response to a pathogen. Certainly the
participants in the current study contended with stressors of considerable
magnitude during the course of this investigation. They included death of a
close family member, the attempted suicide of a close family member, the
death of a pet, a possible recurrence of breast cancer, an auto accident involv-
ing immediate family, impending treatment of a lung fungus, a breast mass
discovered in the daughter of a participant, breast cancer diagnosed in the
sister of another participant, marital problems in the case of two participants,
and serious other health issues for another woman. Therefore, if stress
delays an increased immune response, it is not surprising that these women
showed little immune system improvement during the course of this investi-

gation. If the immune system of more severely stressed individuals delays or

inhibits the synthesis of adequate protective agents, could these women be at

86
higher risk for infection or a recurrence of cancer than less stressed individu-
als?

The stress of the blood draws themselves may have impacted the post
treatment blood counts. All of the women who participated in this study had
experienced multiple necessary previous blood draws and chemotherapy.
Venipuncture was difficult for these women; there were times that the
regular nurse doing the venipuncture would summon a pediatric IV nurse to
complete the draw. Some of the women gave voice to their apprehension and
several of the particiants described the venipuncture as painful. Two of the
women related stories about times during their treatment process when
particlar blood draws or chemotherapy protocols were painful. Rossi (1993)
discusses state-dependent learning as the experience of reverting back to a
former emotional state when similar stimuli are introduced into the present.
One potential participate for this study changed her mind one week later.
She reported reliving the emotional experience of her cancer diagnosis and
treatment that had occurred some years earlier. This woman said that the
emotional upheaval that she experienced after volunteering was so intense
and unsettling that she had decided not to participate.

The stressors on the subjects in this investigation were carefully noted;
however, they could not be controlled or their effects measured. Moreover,
blood assays are available over short time intervals, so the imagery effects
can be monitored only over a limited temporal range. The focus of this study
is the change that occurs when a subject images alone or with her husband.
Therefore the blood analysis data were retabulated according to whether the
specific blood measure increased, decreased, or remained the same (within

10%) following a particular imagery session. The results are presented in

87
Table 11, according to session sequence, and Table 12, according to whether

the subject imaged alone or conjointly.

Statistical Analysis

The statistical method of logistic regression (Hosmer & Lemeshow,
1989; Kleinbaum, 1994) can be employed to analyze relatively small data sets
involving binary random dependent variables, with either binary or continu-
ous independent variables. It has been applied with success to a variety of
clinical studies (Hirsch and Riegelman 1991; Colton et al., 1982-1996).
Logistic regression was employed in this investigation to examine the
relationship of the change in blood cell numerosity (the dependent variable,
or “response”) to the timing of the imagery session (“sequence”), or whether
the session was solitary or conjoint (“alonetog”), with the wife’s session

average IMAGE-CA rating or her DAS score as covariates.

88

Table 11. Post-imagery blood draw numerosities compared to the
corresponding pre-imagery values, sorted by session sequence.

 

 

 

 

 

 

 

Measure Session Couple 1 Couple 2 Couple 3 Couple 4 Couple 5 Couple 6
WBC 1 down down down same down down
2 down same same same up down
Lymphs 1 same down down same down down
2 same down same same up down
CD 3 1 same down down up down down
2 down down same same up down
CD 4 1 same down down up down down
2 same down same same up down
CD 8 1 same down down up same down
2 down down same same up down
Segs 1 down same down down down down
2 same same same down same down

 

 

 

Table 12. Post-imagery blood draw numerosities compared to the corres-
ponding pre-imagery values, sorted according to whether the wife

imaged alone or together with her husband.

 

Measure Session

 

Couple 1 Couple 2 Couple 3 Couple 4 Couple 5 Couple 6

 

 

 

 

 

 

 

WBC Alone down same down same down down
Together down down same same up down

Lymphs Alone same down down same down down
Tngether same down same same up down

CD 3 Alone same down down same down down
Tcgether down down same up up down

CD 4 Alone same down down same down down
Together same down same up up down

CD 8 Alone same down down same same down
Together down down same up up down

Segs Alone down same down down down down
Together same same same down same down

 

90
The LogXact-Turbo computer program (1993, CYTEL Software

Corporation, Cambridge, MA) performs unconditional maximum likelihood
inference, conditional maximum likelihood inference, and conditional exact
inference on the parameters of the logistic regression model. The software
was used to fit the equation:
ln[rrj/(l-arjn = y + 81*variablelj + Bgsvariabler

where arj is the probability that the j-th value of the dependent variable will
be one, and y and the 63 are constants. The null hypothesis tested is that
there will be equal probabilities that the dependent variable will be zero or
one, in which case the logarithmic term will be zero and thus all of the
constants will be zero. The computer program also allows one to test whether
each of the [3 coefficients, or all of them together, are zero (i.e., the constant
term 7 is not treated).

If a particular blood assay numerosity following an imagery session
remained the same within 10% of the pre-imagery value or increased by more
than 10% (same and up in Tables 10 and 11), the response was assigned the
binary value of 1. A decrease by more than 10% (down) was assigned the
binary value 0. Logistic regression was employed to test whether the blood
assay results were statistically dependent on the sequence of the imagery
(binary variable: session 1 = 0, session 2 = 1) and the raters’ average IMAGE-
CA score (continuous variable). The same method was applied to test
conjoint imagery (binary variable: alone = 0, together = 1) and the IMAGE-
CA scores as the independent variables. The marital satisfaction index is
used to encourage subjects with scores below a certain level to consider ther-
apeutic intervention (Spanier, 1976). Therefore, the DAS score was also

tested as a binary covariate. Total scores for the wives equal to or greater

91
than 100 were coded 1; 0 was assigned to lower DAS values. The analysis
was repeated with the wife’s DAS score considered as a continuous variable.

The LogExact data are collected in Appendix K. The parameters in the
logit equation, g and the bs, are evaluated by the method of maximum
likelihood, to give “maximum likelihood estimates” (MLE). In this method
the observed data values are considered to be constants and the parameters
to be evaluated as variables that are fitted using differentiation to find the
values that maximize the likelihood function. Hypothesis tests arising from
unconditional maximization are reported in terms of Likelihood Ratio, Wald
and Scores statistics, with their corresponding p-values. Regression
parameters estimated by this procedure are listed as asymptotic values,
along with their standard errors, 95% confidence intervals, and p-values.
When the values of these test statistics differ substantially, the asymptotic
results may not be valid. Should this occur, another approach, exact
hypothesis testing, is to evaluate the conditional maximum likelihood. This
produces the Exact (Conditional Scores) statistic, exact p-value and mid-p-
value (Barnard, 1990), as well as exact estimates of the logit equation
parameters. The Exact hypothesis test is the preferred treatment for the
data analyzed in this study.

Note that the program tests the null hypothesis that all of the linear
regression constants are zero, or that the coefficients of the variables are
individually or collectively zero. This is equivalent to saying the variables
tested showed no relationship to each other. The significance level of a
statistical test (often chosen as 0.05) is the pre-selected probability of
rejecting the null hypothesis when it is in fact true. If the p-value is smaller

than the significance level, e.g., smaller than 0.05, the null hypothesis is

92
rejected. Here, if p < 0.05 then it is unlikely that the calculated parameter is
zero. Larger values of p are often associated with high standard errors.

The DAS score, first as a binary and then as a continuous variable, was
tested as a covariate with the Sequence of the imagery sessions and with the
Alone/Together condition. For all four models, the null hypothesis cannot be
statistically rejected (See Appendix K). Changes in blood cell numerosities
are statistically independent of the marital satisfaction index rating as well
as of the session sequence and whether the imagery was solitary or conjoint.

The IMAGE-CA score was also tested as a covariate with both the
sequence of the imagery sessions and whether the wife imaged alone or
together with her husband. The corresponding models are: Response =
Sequence + IMAGE-CA, and Response = Alone/Together + IMAGE-CA. Exact
tests were performed on both models on the combined variables and with
each of the variables independently. In no instance could the null hypothesis
be statistically rejected when only the Sequence or the Alone/Together
variable was tested. Exact scores and p-values for the IMAGE-CA variable
alone, except for the segmented neutrophil changes, suggested that the null
hypothesis could be confidently rejected. Segmented neutrophils are
inversely related to lymphocytes, so an incease in neutrophils is anticipated
when lymphocytes decrease. N eutrophils may be observed to increase earlier
than other measures because neutrophils elevate quickly to fight off bacterial
and viral infection. The exact scores improved somewhat, but with moder-
ately poorer p-values, when both variables were considered together.

For all six blood measures, the coefficient for the binary variable that
describes Sequence cannot be confidently taken as non-zero. This suggests
that there is no Significant learning effect on the change in numerosity,

measured on the short time scale surrounding the imagery sessions.

93
Likewise, the coefficient for the Alone/Together variable may satisfy the null
hypothesis. Thus one cannot statistically support the notion that conjoint
imagery has a positive effect on the change in blood cell numerosity, at least
on the time scale of these measurements.

For segmented neutrophils, the coefficient for the continuous variable,
IMAGE-CA score, may well be zero in both the alone/together and first vs.
second conditions. However, logistic regression suggests that the IMAGE-CA
coefficient is confidently non zero for the other five blood variables assayed,
under both conditions. (The responses (binary-coded blood cell changes) for
the limited subject group happen to be the same for lymphocytes and CD4
cells; therefore the LogExact output is identical for these two measures.)
Moreover, the IMAGE-CA coefficient is negative, which implies that a higher
IMAGE-CA score will more likely correspond to a lower probability that the
blood cell numerosity will stay the same or increase following the imagery
session. For white blood cells, lymphocytes, CD3 and CD4 cells, the coeffi-
cient is generally in the -0.04 to -0.06 range. For CD8 cells it is three times
larger (-0.17) in the sequence condition, and ten times larger (-0.55) in the
alone/together condition. Table 13 lists the exact scores and corresponding p-
values, as well as the exact l3 coefficients for the IMAGE-CA and their p-
values, for the two models that included IMAGE-CA as a covariate.

One should View the statistical results from this small sample with
caution. The inverse relationship between the magnitude of the IMAGE-CA
score and the likelihood of blood cell numerosities remaining the same or
improving over the corresponding imagery session (negative 13 coefficient)
may not hold for larger subject groups. In this study that relationship is most
likely dominated by the observations for three couples. The Couple 6 wife
had the highest IMAGE-CA scores in both sessions, yet her blood assay

Table 13. Exact hypothesis test results (conditional exact inference) for two

94

models for changes in blood assay numerosities during imagery

sessions, obtained with LogXact software. (The null hypothesis could

not be rejected for the coefficients of Alone/Together or Sequence.)

Model: Response = Alone/Together + IMAGE-CA

 

 

 

 

 

 

 

 

 

Assay Exact Score p-Value* Exact B(CA) p-Value*
WBC 4.7464 0.0682 -0.0543 0.0200
Lymphs 6.2607 0.0173 -0.0556 0.0133
CD3 4.8194 0.0657 -0.0555 0.0167
CD4 6.2607 0.0173 -0.0556 0.0133
CD8 5.7431 0.0368 -0.5523 0.0050
Segs 3.3672 0.2020 ___ -0.0136 0.2556

Model: Response = Sequence + IMAGE-CA

Assay Exact Score p-Value* Exact l3(CA) p-Value*
WBC 5.3064 0.0505 -0.0358 0.0444
Lymphs 6.2440 0.0216 -0.0555 0.0178
CD3 4.9132 0.0682 -0.0536 0.0233
CD4 6.2440 0.0216 -0.0555 0.0178
CD8 6.0249 0.0390 -0.1661 0.0100
Segs 3.2685 0.2083 -0.0134 0.2333

* One-sided p-values are reported

 

95
counts decreased by more than 10% in each imagery session. The IMAGE-CA

score for the Couple 3 wife was much lower in the Together condition than
Alone, yet all of her blood cell numerosities dropped more than 10% in the
Alone condition and none of them did when she imaged with her husband.
Yet, except for the segmented neutrophil numerosities, the blood assays for
wife 4 always remained the same or went up. Conversely, the Couple 2 wife
had relatively high IMAGE-CA scores in both sessions; except for the Segs,
most of her blood counts dropped more than 10% after imagery in both
conditions. It is noteworthy that the IMAGE-CA regression coefficient for
Segs was the only one for which the null hypothesis could not be rejected.
Moreover, the binary coding for the blood cell changes over an imagery
session masks differences between same and up, and also situations where
the numerosity decrements were different -- but both greater than 10% -- in
the two conditions. In this respect the trends displayed in Figures 8 - 13 may
be more revealing: most of the slopes are negative, which indicates that
imaging in the Together condition led to smaller numerosity decrements, or
greater improvements, with respect to the changes when the wife imaged
alone. Thus there may be an effect of conjoint imagery -- perhaps a smaller
numerosity decrease -- although the coefficient for this independent variable
from the statisical analysis of the experimental data set could not be
confidently ascribed a non-zero value. Clearly, larger sample populations

would provide more reliable statistical results.

CHAPTER V
SUNflVIARY AND EPILOGUE

Summary

Six breast cancer patients and their husbands participated in a 15-day
study that included one solitary and one conjoint guided imagery session. A
baseline blood count and two subsequent pre- and post-treatment blood
analyses were used to measure changes in the wives’ immune systems.

In only 8 of 72 instances did blood counts increase by more than 10%
over the course of an imagery session; in slightly more than half the cases it
decreased by more than 10%. There was no significant two-week learning
effect nor was there statistical support for the notion that conjoint imagery
has an incrementing effect on blood count numerosity over a two-week time
span. For all of the blood measures except segmented neutrophils, the prob-
ability that blood counts will stay the same or increase following the guided
imagery was inversely related to the IMAGE-CA score.

Four participants (wives 1, 2, 3 and 6) showed a decrease in blood
counts, or changes only within 10% of the initial levels, following imagery in
both the alone and conjoint conditions; two of the four showed a smaller drop
in blood counts in the conjoint condition. For two participants (wives 4 and
5), several of the blood assay numerosities increased by more than 10%; this
result was observed only in the conjoint imagery condition, in both cases.
Although the wife of Couple 4 expressed dissatisfaction with the marital rela-

tionship (reflected also in her DAS score), blood counts increased on four of

96

97
the assays and showed less of a decrease on the other two measures in the
conjoint condition.

The first null hypothesis, that there will be no difference between the
baseline measure and the subsequent pre- and post-treatment measures was
accepted. The average pre-imagery blood counts in weeks 2 and 3 were
within 10% of the week 1 baseline measures. The post-imagery averages
were consistently slightly lower, but still generally within 10%. However,
there were opposing trends among subjects. The second null hypothesis, that
there will be no differences between pre-treatment and post-treatment blood
draws was also accepted. As just noted, the post-treatment values were
lower, but on average within the measurement errors. The third null
hypothesis, that there will be no differences between the changes in the blood
counts in the solitary and the conjoint condition was also accepted. On aver-
age, blood counts decreased slightly following both treatment conditions,
although the decrease was somewhat smaller in the conjoint condition. The
fourth null hypothesis, that there will be no relationship between the
IMAGE-CA score and blood count changes was rejected; for all the blood
measures except the segmented neutrophils, higher IMAGE-CA scores were
related to lower probability that blood counts stayed the same or increased
following the imagery. The fifth null hypothesis, that there is no relationship
between blood counts and DAS scores was accepted; no relationship was
found between DAS scores of the wife and the blood count differences
between the solitary and conjoint conditions.

This study supports the conclusion that blood counts do not increase
over the immediate course of an imagery session or over the duration of 15

days. It is possible that the effects of behavioral intervention such as guided

98
immune system imagery occur over a longer time interval than the 15-day

period employed in this investigation.

Recommendations for Future Work

The uniform decreases in lymphocyte counts following imagery
measured in this investigation (although generally within measurement
error) are consistent with results in the few published studies evaluating
short term blood cell changes following behavioral treatment (Rider and
Achterberg, 1989; Zachariae et al., 1990; McGrady et al., 1992; Hall et al.,
1992; Zachariae et al., 1994). Behavioral studies of longer duration (6 weeks
to several years) have Shown increases in lymphocytes over time (Gruber, et
al., 1988; Spiegel et al., 1989; Fawzy, et al., 1990; Fawzy, et al., 1993; Gruber,
et al., 1993). Clearly, a longer-term study is indicated.

A pre- and post-treatment blood measurement design (with controls
matched for age and gender) conducted over a 12 - 18 month period of time
would yield both short- and long-term changes for both the solitary and
conjoint conditions, for the participating subjects and their controls.
Assessments of marital satisfaction, social support and depression could be
made at intervals throughout the study. In addition, participants could be
given the treatment audio tape and be encouraged to practice and record the
number of rehearsals between sessions and blood assays. If adequate funding
were available, a large number of subjects (forty in each group) would allow
for more generalizable results. The possibility of cumulative effects of
imagery in each treatment condition could be studied in this design.

Without exception, the cancer patients who volunteered to have their
blood drawn for this study presented with veins damaged by prior necessary

medical diagnostics and treatment, leading to considerable discomfort during

99

some of the venipunctures. Did the stress accompanying the anticipation
and consummation of the blood draws impact the blood assays? A study that
isolated the stress of venipuncture from a treatment condition in healthy and
ill populations would yield useful information. If four groups of subjects
(healthy subjects/alone, healthy subjects/with a spouse, cancer patients who
had completed chemotherapy/alone, cancer patients who had completed
chemotherapy/with a spouse) had two blood draws (the same time of day for
each subject in each group), one hour apart with no intervening experimental
treatment, variations in repeated venipuncture for healthy people and cancer
patients, alone and with a spouse, could be measured.

There are numerous studies supporting the idea that stress impacts
the immune system (Andrews & Tennant, 197 8; Baker et al., 1984;
Borysenko & Borysenko, 1982; Bowers & Kelly 1979; Glaser et al., 1992;
Guillemin, Cohn, & Melnechuk, 1985; Kennedy, Kiecolt-Glaser, & Glaser,
1988; Kobasa 1982; Locke 1982; Locke et al., 1984; Manuck, Cohen, Rabin,
Muldoon, & Bachen, 1991; Plaut & Friedman 1985; Sklar & Anisman 1979;
Stein 1985). The relationship between stress and immunity needs careful
study. Following stress, some immune system measures reportedly increase
(Cacioppo et al., 1995; Sgoutas-Emch et al., 1994) while others decrease (Feng
et al., 1991; Glaser et al., 1992; Jemmott & Locke, 1984; Kennedy, Kiecolt-
Glaser, & Glaser, 1988; Kiecolt-Glaser, Glaser, et al., 1987; Kiecolt-Glaser &
Glaser 1992; Kleinbaum, 1994; McKinnon, Weisse, Reynolds, Bowles, &
Baum, 1989).

Manuck and his colleagues conducted correlational studies that found
psychological stress to suppress cellular immune function in some individuals
and not others (Manuck et al., 1991). Differences in types of stressors,

variability in perceptions about that which is stressful, the variability in

100
immunologic responses to stress and the impact of acute stress versus
prolonged chronic stress (as well as the immediate effects and the long term
effects) all warrant further study.

Additional work in these areas will help clarify whether enhanced cell
counts and activity are healthy processes or unhealthy processes. Perhaps
the relative merit or liability of cellular changes is related to whether they
are long or short term effects. Ultimately we need to learn how alterations
in cellular immunity affect long term health.

The present investigation showed cellular response differences among
individual subjects when the wife imaged alone and when she imaged with
her husband. Blood measures following the conjoint condition, regardless of
how one might interpret the relative merit of the imagery condition,
increased for two participants and generally decreased less for the others.

Would cellular changes increase if a subject did the imagery with her
whole family? Would the presence of the whole family impact the blood
counts independent of the imagery condition? Would prayer for an immune
system capable of maintaining health be more effective than Specific immune
system imagery? Would imagers unknown to the patient affect a woman’s
immune system in the same manner as the husband co-imager? Would
experimentor-generated images known to be powerful have the same effect as
participant-generated images (the method used in this study)?

The field of psychoneuroimmunology has been emerging over the past
twenty years. It is now clear that the immune system does not operate
autonomously, as once supposed. Further, the immune system is not a closed
entity; behavioral and psychological processes impact immune function. The

objective to understand the interactions between behavior and the immune

101
system offers many diverse and interesting future research projects in the

realm of psychoneuroimmunology.

APPENDICES

APPENDIX A

Dyadic Adjustment Scale

APPENDDC A

DYADIC ADJUSTMENT SCALE
Most persons have disagreements in their relationship. Please indicate below the approx-
imate extent of agreement or disagreement between you and your partner for each item on

the following list.

Always

Amie
. Handling family finances

Almost
Always

 

. Matters of recreation
. Religious matters
. Demonstrations
of afl’ection
. Friends
. Sex relations
. Conventionality (correct
or proper behavior)
8. Philosophy of life
9. Ways of dealing with
parents or in-laws
10. Aims, goals, and things
believed important
11. Amount of time spent
together
12. Making major decisions

IbOONI-t

donor

 

 

13. Household tasks

14. Leisure time interests
and activities

15. Career decisions

 

All the
time

16. How often do you discuss
or have you considered
divorce, separation, or
terminating your rela-
tionship?

17 . How often do you or your
mate leave the house
afier a fight?

18. In general, how often do
you think that things
between you and your
partner are going well?

19. Do you confide in your
mate?

20. Do you ever regret that
you married (or lived
together)?

21. How often do you and
your partner quarrel?

 

illllll HI Hi

 

Most of

102

Occa-
sionally

Fre-
quently

Almost
Always

Always

 

 

 

More often

Occa-

thannotsicnellx

New;

103

22. How often do you and
your mate “get on each
other’ 8 nerves”?

 

 

Every Almost Occa-
Dax Emilia! stencils Bambi New

23. Do you kiss your mate?

 

 

 

All of Most of Some of Very few None

them than them affirm cflthem
24. Do you and your mate engage

in outside interests together?

 

 

 

How often would you say the following events occur between you and your mate?
Less than Once or Once or
once a twice a twice a Once a More
Nae: month month week star often
25. Have a stimulating
exchange of ideas
26. Laugh together
27. Calmly discuss
something
28. Work together on
a project __ _
These are some things about which couples sometimes agree and sometimes disagree.
Indicate if either item below caused differences of opinions or were problems in your
relationship during the past few weeks. (Check yes or no.)

lee Ne

 

 

 

 

 

29. Being too tired for sex

30. Not showing love

31. The dots on the following line represent different degrees of happiness in your
relationship. The middle point, “happy”, represents the degree of happiness of most
relationships. Please circle the dot which best describes the degree of happiness, all
things considered, of your relationship.

 

 

 

 

Extremely Fairly A Little Happy Very Extremely Perfect
Unhappy Unhappy Unhappy Happy Happy

32. Which of the following statements best describes how you feel about the future of your
relationship?

I want desperately for my relationship to succeed, and would go to almost
any length to see that it does.
I want very much for my relationship to succeed, and will do all I can to see
that it does.
I want very much for my relationship to succeed, and will do my fair share to
see that it does.
It would be nice if my relationship succeeded, but I can’t do much more than
I’m doing now to help it succeed.
It would be nice if it succeeded, but I refuse to do any more than I am doing
now to keep the relationship going.
My relationship can never succeed, and there is no more that I can do to keep
the relationship going.

APPENDDI B

Laboratory Blood Test Procedures

II.

APPENDDI B
Laboratory Blood Test Procedures

Immunology laboratory
Sparrow Hospital
Page 1 of 7

WHOLE BLOOD PROCEDURE FOR DIRECT IMMUNOPHENOTYPING OF
LEUKOCY'IE SUSPENSIONS

ems:

Peripheral blood leukocytes can be divided into several distinguishable subpopulan'ons: T
cells (thymic - dependent), which are involved in cell-mediated immune responses; B cells
(Bursa-dependent), which are involved in humoral immunity; Myeloid cells, granulocyu'c
series; and Monocytic cells. Several of these populations of leukocytes are not easily
distinguishable by morphology or by histochemical Staining alone, but can be identified by
antigenic markers associated with the plasma membrane. Monoclonal antibodies have been
developed that react with antigens expressed on mature and immature lymphocytes,
myeloid cells, and monocytes. likewise, antibodies exist which can distinguish
subpopulations of some of these cells such as the helper/ inducer and suppressor/cytotoxic
subpopulations of T-cells.

Enumeration and characterization of leukocytes by their surface membrane markers are
useful in: 1) analysis of an increased number of, or phenotypically abnormal cells from
peripheral blood, bone marrow aspirates, or lymph node biOpsies, 2) the study of patients
with a suspected primary immunodeficiency, and 3) the study of acute and chronic diseases
associated with altered immune funcu’on including acute infection, allergy, leukemia, and
lymphoma.

The reagents for direct staining the cells are already conjugated with FITC or Phycoexythrin
(PE). These antibodies incubated with whole blood, the cells washed, lysed, and fixed with
paraformaldehyde. Specimens may also be plated on microscope slides and evaluated using
a fluorescent microscope.

Venous blood is collected by venipuncture into ACD or sodium heparinized tubes. One
normal control musr be tested with each batch of patients. Bone marrow specimem should
be heparinized. Blood collecred with other anticoagulants and clotted blood are
unacceptable.

Bone marrow must be washed once with "Incomplete H388" and filtered through 37
micron mesh if fibrin Strands or clots are present. A white count on the can be performed
on the Smith-Kline ESKA Lab cell counter and the WBC concenu'ation adjusted to 10-20
X 10‘/ml using Incomplete HBSS.

Validate the name and time of collection of specimen by checking the specimen and paper
work that accompanies it, if any discrepencies, notify Immunology supervisor immediately.
Specimens must be shipped and stored at room temperature until used. Specimens older
than 24 hours must have a viability test performed (see lymphocyte preparation and
viability sections) to determine quality of specimen. Viabilities less than 85% must be
interpreted with extreme caution.

104

T&BDirect

III.

105

Immunology laboratory
Sparrow Hospital
Lansing, Michigan

Page 2 of 7

-SPE UPP ANDE UlP :

Whole Blood Lysing Reagent Kit - Coulter Immunology, Hialeah, FL #6603152. This includes
Coulter Clone Immuno-Lyse Concenu-ate and Coulter Clone Fixative. ImmunooLyse must be stored
at 2-8°C. Fixative must be stored at room temperature.

1X PBS-pH 7.4 Sigma, St. Louis, MO. Cat 1000-3.

Monoclonal Ann'bodies - Becton-Dickinson Immunocytometry Systems, Mountain View CA.
Coulter Immunology, Hialeah, FL.

10% Na Azide - 10gm of Na Azide dissolved in 100ml of nanopure water. Store 2-8°C.

Hank’s balanced Salt Solution (CaH and Mg” free) - "Incomplete" Hank’s, GIBCO, 310-4175.
Shelf life one year. Use at room temperature.

IEC Clinical Centrifuge - Damon/15C, Needham Hgts, MA.

Beckman Centrifuge Model TJ-6.

12 x 75mm plastic test tubes - Falcon, Oxnard, CA. Obtain through Sparrow Stores.
Micropipettors - Medical Laboratory Automation, Mount Vernon, NY.

Smith-Kline ESKAIAB (CBS-3T) Smith-Kline Diagnostics, Inc.

Vortex Mixer, Scientific Products, 58223-1.

FACSCAN, Becton-Dickinson, Mountain View, CA.

 

Manadgnalmu’hgdics

CLUSTER BECI‘ ON COULTER DISTRIBUTION
DESIGNATION DICKINSON

CD2 Leu 5b T11 T cells, NK cells

C03 Leu 4 T3 T cells

C04 Leu 3a,b T4 Helper/Inducer, Monocytes
C08 Leu Za,b T8 Suppressor/Cytotoxic, NK
CD10 CALLA J5 Pre-B cells

C013 Leu M7 MY7 Monocytes, PMN’s

C014 Leu M3 M02,MY4 Monocytes

C015 Leu M1 PMN’s, Reed-Sternberg
CD16 Leu 11a,b NK cells, PMN’s

C019 Leu 12 B4 B cells

106

 

T & B Direct Immunology laboratory
Sparrow Hospital
Lansing, Michigan
Page 3 of 7
CLUSTER BECTON COULTER DISTRIBUTION
DESIGNATION DICKINSON
C020 Leu 16 BI Early B cells
C033 Leu M9 MY9 Monocytes, PMN’s -
C034 HPCA-I Progenitor cells
C045 HLE KC56 All Leukocytes
C056 Leu 19 NlG-l-l NK cells, T subset

Detailed monoclonal antibody specificity information can be found by reviewing Beaten-Dickinson
Monoclonal Source Book and the Coulter Clone Monoclonal Antibody Book.

Monoclonal antibody expiration dates are provided by the manufacturer. Monoclonal antibodies
can be used past expiration date if normal control values fall with the normal ranges.

NW

Normal peripheral blood from a co-worker that has been tested previously and showed to
be normal for T 8: B cell populations is used with each analysis. Each individual
monoclonal antibody that is being tested on a patient should also be tested with a normal
control. Values should fall within established ranges for the particular antibody. If the
values fall outside the normal ranges, notify Immunology supervisor or director. The
staining of the patient will have to be repeated in parallel with a new mg $913121.

3
1.

mm

tio :
PBS-Azide - Prepare working solution of phosphate buffered saline (1X). Add 0.1% Na
Azide (Sml of 10% Na Azide per 500ml PBS). Store at 28°C. Good for 6 months.

Immuno-Lyse working solution: One ml of Immunolyse working solution is required for
each test tube. Prepare working solution of ImmunoLyse by adding ImmunoLyse to PBS
Azide at a 1:25 dilution (40ul of Immuno-Lyse per 1ml of PBS Azide). See following
panels to determine number of tubes to run, and therefore the number of mls of working
solution that will be necessary.

1.

Label 12 X 75mm plastic test tubes for the appropriate panel. Make one line tape labels
listing the initials of the person whose specimen will be in that tube (first name first) and
the antibodies to be tested in that tube (green first, then red). If there is more than one
type of specimen on one person, e.g. peripheral blood and bone marrow, also indicate on
the label which specimen is being tested in which tube. For example, when testing John
Smith’s bone marrow with the CD3-Green and C019-Red antibodies, the label would read:
JS 3/ 19 BM.

107

T 8: B Direct Immunology laboratory
Sparrow Hospital

Page 4 of 7

2. Prepare working solutions of the monoclonal antibodies. The appropriate dilution is one

test volume of antibody (generally 20ul) into 200ul of PBS-Azide. Make these dilutions
in the 12 X 75m tube in which the test is to be performed.

3. Add 100ul of the venous blood sample or diluted washed bone marrow to each tube.
4. Vortex vigorously. Always vortex with tubes covered or in the hood.
5. Incubate at room temperature for 30 minutes.

6. Wash with 3-4 ml of cold PBS-Azide in IEC Clinical or Beckman TJ-6 Centrifuge. IEC speed
#3 for 3 min, Beckman 1200 rpm for 45 minutes. Aspirate supernatant carefully, and
vortex vigorously. Repeat for a total of 2 washes.

7. While vortexing, add 1 ml of Immuno-Lyse working solution to each tube, allow tubes to
sit no less than 30 seconds, and no longer than 1 minute before proceeding to step #8.
(Stay as close to 30 seconds as possible, especially on leukemic specimens.).

8. While vortexing, add 250 ul of fixative.

9. Wash cells 3 times with PBS-Azide as in step 6. Aspirate supernatant.

10. Add 200 ul of PBscAzide and vortex.

11. Store tubes, capped, in the refrigerator until flow cytometry analysis is performed.

W W

Please refer to the FACSCAN Set-Up/Maintainence manual for setting up the instrument using
Calibrite Beads in the Autocompfunction.

VII-W

The FACSCAN flow cytometer has a software switch which can make a quadrant correction
when the percentage of lymphocytes within the analysis gate is less than 100%. This
switch is my used when performing CD4/C08 ratio analysis and absolute lymphocyte
counts.

The calculation performed is:

QW percent
CD45% - CDl4%

108

 

T&BDimcr Immobgylaboratory
SparrowHospiral

I O ’!!C]I
PageSof7

VIII-W

Normal Ranges:

Mala Ecnnhstalfllggé W mm 0 :5
CD2-CD3 52-90% 5-50% 50.80%

(:04 28-63% 580% 20—6096

CD8 8-38% 5-20% 840%
CD4-C108 1-5 -- —--

HlA-DR 240% 5-30% 5-30%

CD19 2-20% 2-20% 2-20%
C013-CD33 2-20% 540% ND

Using the antibody combinations CD3/C04, CD3/C08, and CDS/l-ILA-DR one can better
interpru the data from the flow cytometer (See figures 1-3 below):

Figure 1

Use only quadrant 2 for CD4

Add quadrant 2 and 4 for CD3
CD3 I CD4
1 2 I=MONOCYTES

2=CD4+
3=B CELLS. ECT

D Q m...

 

 

 

 

109

 

 

 

T 8: B Direct Immunology Laboratory
Sparrow Hospital
Page 6 of
Figure 2

CD3 [c133 Use only quadrant 2 for C08

1 2 2:381:31: Add quadrant 2 and 4 for CD3
3—a can: . arc
.. 4-cnev

3 4

 

 

 

Figs: 3
CD3 [BIA-DR Use q¥aéi$snr 2 for acnvated
1-. cars, are Add quadrant 2 and 4 for total
1 2 2am r cm: T cells
.. yum ems
. . «um I ems Add quadrant 1 and 2 for toral
activated cells
3 4
O 9

 

 

 

 

110

T 8: B Direct Immunology laboratory
Sparrow Hospital
Lansing, Michigan
Page 7 of 7
IX- W

Coulter Procedure for Indirect Immunofluorescence Cell Surface Staining with Coulter Clone
Antibodies (Whole Blood Quick- Stain).

X- W
Prepared by: Faisal Rawas

Adopted

Reviewed

Reviewed

Reviewed
Revised 9/15/92

 

I:\WP51\PRTCL\T&BDIREC

APPENDIX C

Immunoimagery Videotape Script

APPENDIX C

Immunoimagery Videotape Script
(16 minutes)

[Transcribed from: Bioimagery. (1993). The science of immuno-imagegz.
Irvine: Bioimagery]

Science has shown that there is a direct link between the mind and the
immune system. By using positive imaging techniques we can take an active
role in strengthening our immune system to fight disease and promote good
health and well-being. This science is called psychoneuroimmunology. The
following information is an overview of the biological processes by which the
mind, the nervous system and the immune system interact, and will provide
you with excellent visuals for use in immunoimagery.

The wonder of life with its distinctive order and intelligence is as
awesome as it is mysterious. Millions of years ago, perhaps by accident,
perhaps by design, life itself was born from the smoldering embers left from
violent explosions between dense gaseous clouds. All organisms, plants, and
animals...all things 1iving...can trace their ancestry back to the first single
cell bacterial organism formed over three and one-half billion years ago. The
genetic code of all living beings is composed of the same base elements. The
same twenty amino acids build our proteins. The same combustion systems
change food into energy. Scientists and philosophers alike have called this
common denominator the blueprint of life. Through our creation nature has
demonstrated a remarkable resiliency, a desire to survive and adapt to the
changing environment. No life form on earth has demonstrated this
resiliency, this adaptability, more than humankind.

The most complex manifestation of nature's creations is the human
body. We are a living machine, more intricate and finely tuned than the most
sophisticated computer. With an elaborate system of fluids, organs,
chemicals, and electrical currents that define the human existence. The
wisdom of the body, the blueprint of life, is truly represented in its ability to
defend and heal itself. Over millions of years evolution of the human body
has created a remarkable self-adjusting balance. This internal equilibrium
can adapt itself to a temperature change of even a fraction of a degree,
bathing the body in cooling perspiration when it becomes too warm. When
too cold it begins to shiver, converting energy into heat.

The body consists of several primary systems such as the skeletal and
the circulatory systems. Our immune system maintains our health, enabling
us to fight a cold, ward off infection, or close a cut in our skin. The brain and
nervous system is a perfectly coordinated mechanism that efficiently

111

112

regulates the body's life support systems. The human brain with infinite
complexity is the most intricate structure known to man. Its potential power
and many mysteries are still largely unknown and misunderstood. What
uniquely separates humans from all other life is our knowledge of the self,
the mind's own ability to be aware of itself. As awareness of the self has
evolved, we've begun to understand the power of the mind and its influence
over the body's health and well-being. Our natural environment contains
many potentially harmful micro organisms that can enter the body and
attack healthy cells. When this happens, we get sick and the immune system
reacts to fight these harmful microbes and restore good health.

In a process not completely understood, the brain, through the immune
system, helps the body recognize healthy cells while destroying and removing
diseased cells. This internal intelligence, recognition of the self and non-self
at the microscopic level, is one of life's greatest wonders. We've experienced
many examples of our mind altering a physical change in our body in some
way. For example, when watching a movie we often react as though the on-
screen action is real. Dreams are another good example of the mind creating
an image we often perceive as reality. The action doesn't happen to us, but
the physical response does. A placebo can cause some people to respond to an
illness in a positive physical way, even though there is no actual medicine
involved. It is this type of stimulus-response that is the key to stimulating
our immune system through the power of our own mind. This
communications network between the mind and the body can literally be
guided by a process called imagery. By mentally rehearsing or imagining our
natural self healing processes at work, the immune system is stimulated.
Researchers documented that the cells, tissues, organs, and muscles involved
in the healing process become stronger or work more efficiently using positive
imagery. The opposite is also true. Negative attitudes and stressful
situations can weaken our immune system.

Imagery has been used successfully in medicine with impressive
results. Imagery is regularly used by physicians treating cancer patients in
conjunction with standard forms of medical therapy. To fully utilize
immunoimagery it's important to have a basic understanding of the immune
system. Let us take a guided tour through this process and actually see the
body's immune system fighting disease.

The brain controls and integrates all body mechanisms through forty-
three pairs of nerves leading to separated parts of the body. Different areas
of the brain automatically control life support systems such as breathing and
heartbeat as well as the intricate workings of the immune system. Neurons
are the fundamental working units of the brain, spinal cord, and the nervous
system. The brain alone has ten thousand million of them. The task of
neurons is to generate, send, and receive signals through electrical and
chemical impulses. Our thoughts are the results of these impulses. After the

113

brain translates the impulses received from different sensory neurons, it
sends the proper response impulse back to the stimulated area. When the
body needs food, for example, neurons send a message to the brain and
stomach that translate into hunger. Through this intricate network, the
brain communicates with the immune system to regulate our health and
defend the body from harmful invaders.

Organs in our bodies such as our lungs, are made up of layers of tissue
which in turn are made up of cells, the body's smallest living unit. During
life's normal activity, healthy cells divide and grow as this time-lapsed
photography shows. However, when harmful microbes enter and attack
healthy cells, an immune response is triggered. The immune system is a
miraculous mechanism of defense, attack, and repair consisting of a network
of cells and organs that instantly respond to the presence of any disease-
causing intruder. At all times the immune system surveys the identifying
chemical markers of every molecule and cell in the body. Using these
markers, the immune system can identify harmful cells and launch a full-
scale attack to destroy them. Viruses, parasites, fungi and bacteria are
common substances that trigger such an attack. These natural enemies often
enter the body when we are undernourished, exhausted, or injured. Our
skin, the body's first line of defense, keeps most of these harmful microbes
out; however, they can enter through our eyes, nose, lungs, and throat.

The immune system is not contained within a single set of organs or
vessels. The intricate networks of the lymphatic and the circulatory systems
carry the cellular components and chemical messages of the immune system
throughout every part of the body. Its army of defenders, white blood cells,
can even pass single file through the body's smallest capillaries, to reach the
battle area of infection. Over a trillion or more of white blood cells are
present in nearly every tissue and organ. White blood cells originate in the
bone marrow inside our long bones such as the legs and arms. Bone marrow
produces millions of white blood cells daily, which are further programmed to
patrol, attack, and destroy enemy invaders. They lead the body through the
tiny vessels that feed them and sometimes travel to other glands to continue
development such as the thymus or the spleen. The two main groups of white
blood cells are called macrophages and lymphocytes. Other types of white
blood cells amplify their effects. Macrophages destroy invader microbes and
diseased or infected cells by eating them. Macrophages hunt their prey
incessantly and can change their shape to pursue and devour a diseased cell.
Here a macrophage uses its arm-like pseudopod to engulf and destroy a
harmful invader. Macrophages are also called your body's trash collectors as
they routinely consume pieces of dead tissue left over from the natural
destruction of invading micro organisms. These materials are removed from
the body to the lymphatic organs and vessels, an integral part of the immune
system.

114

Lymphocytes are the white blood cells that distinguish between
healthy cells and harmful invader cells. T-cells are lymphocytes that
coordinate and communicate with the other types of cells in the immune
system. Once T-cells become sensitized to the invader, they begin to
multiply. Another subgroup, the killer T-cells, attack and destroy cancer cells
and infected body cells before they have a chance to multiply. Another
important lymphocyte, B-cells, produce a kind of chemical ammunition called
antibodies. Antibodies are individually programmed by the brain's internal
intelligence to seek out and destroy specific target microbes. Here a B-cell
surrounded by harmful microbes uses its antibodies to destroy the enemy
invaders. Together the macrophage and lymphocytes of the immune system
win the battle against disease by destroying the enemy microbes. New cells
divide and grow to replace damaged cells and good health is restored.

Now that we've seen our immune defenders under the microscope, let's
recreate this process using animated characters. These lymphocytes have
been color-coded for easy identification. The commander T-cell is orange, the
killer T-cell is green and the B-cell is blue. Macrophages, the body's
scavengers, are colored yellow. Harmful microbes enter the body and move
into the bloodstream. The invader attacks and infects a normal cell telling it
to produce more harmful agents. The immune defenses are triggered into
action as macrophages first recognize that the intruders are not part of the
body. Macrophages help T-cells identify the type of foreign invader using an
identifying chemical handle on its surface. Once they have identified the
enemy, T-cells become the commander, giving orders to the rest of the defense
troops. The encoded T-cell then multiplies and creates specialized defense
troops to fight the invader. Killer T-cells are signaled and search for infected
cells to attack and destroy. Chemical messages are sent to alert other
macrophages of the invasion. Other messages direct the macrophage to the
battle area and help them attach to the infected cell. Finally, they eat and
digest the invaders, making them harmless. If the immune defense troops
become weak or are outnumbered, the commander T-cell sends for more
powerful troops. Among these new recruits are the B-cells which produce and
release antibodies, chemical bullets that destroy harmful microbes.
Antibodies also stick to infected cells which help killer T-cells and
macrophages quickly identify and destroy them. Helper cells spring into
action by helping B-cells make more antibodies. When enough antibody has
been made, suppressor cells direct B-cells to halt the production. Platoons of
natural killers, NK cells, also search the area, killing infected cells and
cancer cells. The immune defense troops--T-cells, B-cells, macrophages and
others-finally destroy and remove the invader microbes. The long battle is
ended as the immune system is again victorious. Gradually damaged cells
are replaced by new cells and good health is restored.

APPENDDC D

Immunoimagery Audiotape Script

APPENDIX D

Immunoimagery Audiotape Script
Music - Sounds of the Heart by Karunesh

My voice will guide you into a relaxed state. You will be invited to
imagine with music your body's resourcefulness in fighting cancer. Each
person does this a little differently, so trust your instincts and ignore any
suggestions from me that don't seem to fit with you. We're ready to begin.

The first thing you'll want to do is just make yourself comfortable in
your space. You might want to wiggle around a little or loosen anything
that's tight. That's it...just whatever you need to do to feel comfortable. You
can either make soft eyes or close your eyes...whatever you prefer. Take in a
deep, deep breath through your mouth, hold onto it just a little, and let it go
out through your mouth. You'll probably notice that there are some sounds in
the room and just take note of them, and recognize what they are and let
them fade gently into the background. You might want to take another deep
breath, in up through the bottoms of your feet, up your legs, your torso, and
out your mouth. That's it...just take another deep, deep breath, in and up,
and out your mouth. You might even note a heaviness begin to set in your
arms and legs as you breathe. And with each breath, you spread a calm,
peaceful feeling. Your breath carries the tension out of your body as you
exhale. So you can breathe in a peaceful, relaxed feeling. Let it seep the
tension out of your body. Breathe in relaxed peacefulness...breathe out the
tension. Just focus on breathing in the peacefulness...breathing out the
tension. By now your feet may be feeling far away, as though your legs are
ten feet long. Perhaps your hands are feeling far away at the end of your
long, long arms. Breathe in a sense of calmness...breathe out tension.
Breathe in...breathe out.

As the music begins I invite you to journey to a place deep inside
your/your wife's body...to a place where all things are possible. You might
have to become very small and find an opening in the body to squeeze
through, but you'll be able to find a way in, and once you're in, you'll be able
to move easily around the body and experience first hand those places where
little miracles occur. The places where everything is possible. Take a
moment to enjoy the panorama of color. What does it sound like in there?
Does this inside space have a taste? What does it feel like to reach out and

115

 

116

touch these spaces as you move along? Do you have sense of rhythm or
movement in this space?

As you journey inside your/your wife's body, you become aware of the
production of millions of new white blood cells in the bone marrow. All up
and down the bones, new white blood cells are made daily. It might not look
anything like you imaged it would. What colors are the white blood cells? Do
they make any sounds? If you reach out and touch them, what do they feel
like? What if you tasted one? Do you have a sense of how the white blood
cells move as they multiply?

There are different kinds of white blood cells and each kind is
specialized to perform a specific job in fighting cancer. Take a moment to
witness the white blood cells multiply purposefully, yet freely and easily as
they dance in huge companies throughout your/your wife's blood stream...out
of the blood stream and into every organ, tissue, and bone.

(pause)

It's unbelievable how fast they multiply and how far reaching their
travels are. They are so clever...white blood cells can slip out of the blood
vessels and into the bones, tissues, and organs. White blood cells are truly
amazing. Just be with that process for a little while. Watch them multiply
and multiply.

(pause)

Some of those specialized white blood cells are T-cells. Take a moment
to notice your/your wife's T-cells. They're the ones that are trying to get your
attention. Notice what color the T-cells are. If you tasted them, what would
that be like? Do the T-cells have an odor? If you touch the T-cells what
would they feel like? What would it be like to be a T-cell and move all
around?

(pause)

Your body/your wife's body can produce many, many new T-cells. T-
cells are smart. They have the ability to travel around the body and find
cancer cells. T-cells are so smart, they do not attack self. They only attack
foreign invaders like cancer. T-cells are playful while they multiply and
they're playful while they find and destroy cancer. T-cells flourish when you
are relaxed. T-cells are very good communicators. They organize the

 

117

immune system and they notify each other as well as other white blood cells
when they find malignant cells in the body. T-cells also have incredibly long
memories. They always remember what the foreign invaders are like. They
immediately go to them and surround them. They can find very tiny, itty-
bitty cancer cells. They call for help from other white blood cells and T-cells
and together surround the foreign invaders and eat the cancer cells up,
rendering them harmless. When T-cells are active and do their job, the body
feels relief from pain. Take some time and watch the T-cells remember, go to,
surround, eat, and digest cancer.

(pause)

They're capable of quite a process. Now let's take a moment to get a
sense of what cancer cells are like. Some people believe that all of us have
malignant cells in our body and that daily our white blood cells remove them
before they can multiply. Cancer cells are confused and weak. They multiply
like rabbits because they don't know any better. Take a moment and see if
you can get a sense of what color cancer cells are? Where in your body/your
wife's body would you look for cancer cells? Do the cancer cells emit a noise?
Can you smell the cancer cells? Take a moment to scan your body/your wife's
body looking for cancer cells. They're the ones that are stupid. They're
foreign matter. Cancer cells have no purpose and cancer cells have no
conscience. Just look around and see if you can spot them.

(pause)

Now take a moment to remember some of the resources that help
you/your wife to fight off cancer cells...resources that are outside the body.
There may have been medical treatment, kind thoughts from friends and
family, prayers, love, people cheering you on...healing, hopeful images from
others. Where are these supportive resources held within your body/your
wife's body? Do they have a color? Do they move around? What might they
sound like as they embrace your healing? Are there any smells associated
with these resources? Just take some time to get a sense of the external
resources to help you fight cancer, and what they feel like in your/your wife's
body.

(pause)
We've spent some time getting a sense of your/your wife's internal cancer-

fighting process, and the sense of either your or your wife's external
resources. Now feel the strength and power of your wife's or your internal

118

cancer-fighting process working in harmony with your/her external resources.
It can be a very, very powerful combination.

(pause)

Witness the T-cells multiply and enthusiastically move from where they are
manufactured into your/your wife's bloodstream, tissues, and organs. The T—
cells take command of the body's defenses and are cheered on by family,
friends, and professionals. The T-cells skillfully identify and mark cancer
cells for destruction. T-cells playfully, yet efficiently move to cancer cells and
mark them with a chemical. After the diseased cells have been marked,
armies of other T-cells are called upon to swarm to the marked cells, and
when they get there, the T-cells have a lot of fun cooperating together
throughout your/your wife's body. They engulf cancer cells, eat them, and
digest them, making them harmless. Take some time to be with them as they
accomplish important, healthful, playful work. Remember all the outside
resources and people cheering those T-cells on, cheering the immune system,
supporting all the work you're doing.

(pause)

They multiply. They move from where they're manufactured into the
bloodstream, tissues, and organs and they're cheered on by a host of family,
friends and professionals. They're skillful...playfi11...smart.

(pause)

You can manufacture however many you need to get the job done. Their
memories guide them to the diseased cells. All the new ones sort of carry a
collective memory from the others that preceded them, so they know exactly
where to go in the body to find diseased cells.

(pause)

T-cells also have the knowledge of when there are enough of them. Then they
stop producing and multiplying so many times when they know that they
have enough to be effective and they slow down the multiplying process.

They'll know that.

(pause)

119

You may wish to encourage your/your wife's T-cells to keep on multiplying as
needed...to keep on searching...to continue marking cancer cells...and to help
each other swarm, engulf and destroy the diseased cells. Perhaps you'll
discover a way for the T-cells to continue their work in your/your wife's body
after our journey has ended.

(pause)

Time is approaching for us to end this journey inside your/your wife's body.
You might want to retrace some of your steps...pause along the way to re-
experience some of your images and to remember what they felt like. And if
you wish you can store these sensations in a special place in the body where
you can easily find them again.

(pause)

It's time to end our journey and to gradually return to this room. As the
music ends you may want to take a deep, deep breath in through the bottoms
of your feet and out your mouth. And with another deep breath notice the
sounds of the room creep back into your awareness. With each succeeding
breath the sounds associated with this room might be coming closer and
closer. And you might feel like you can feel your hands and feet
again...become aware of what they feel like. You might even have the urge to
wiggle your toes or shake out your hands. You can stretch a little. When you
feel ready, you can open your eyes and have a look around.

APPENDIX E

IMAGE-CA

APPENDIX E

 

lMAGE-CA
Interview Record and Scoring Sheet

Jeanne Achterberg and 0. Frank Lawlis

 

 

 

 

 

 

Biographical/Treatment Data

Age __ Sex __ Marital Status
Type of Treatment: 0ate(s):

Surgery 0n

Radiation from to

Chemotherapy/lmmunotherapy from to

Other lrorn to
Diagnosis: Primary Site

Secondary Siteis)

 

1) no evidence at disease
2) disease stabilized
3) continued active disease

Current Disease Status:

 

 

 

IMAGE-CA Total Score (sten)

 

 

MOW

This booklet is designed for recording and scoring information obtained irom patient imagery
drawings. After the patient has listened to the guided relaxation exercise. instruct him/ her to draw.
on a separate 8% x 11-inch sheet of white paper. (1) the white blood cells. (2) any treatment being
received. and (3) the cancer cells. all acting inside the body. When the drawings are completed.
begin the interview using pages 3 and 4.

Following the interview. score the imagery/interview content according to the 14 Dimensions
appearing on page 2. Alter scoring each at these scales. told in page 4 so that MACE CA -
Sunny Date (9890 5) is directly opposite page 2. Transcribe the 14 scores in column (2) or the
table provided on page 5. Next. multiply each at the individual scores by the weights appearing in
column (3) and enter the product in column (4). Add these components and enter the sum in the
appropriate box marked “Weighted Sum." Then. in the appropriate sten conversion table. iind the
interval containing the obtained weighted sum and the corresponding sten score.

INSTITUTE FOR Pensouaurv mo ABILITY TESTING. me.
P. 0. Box as. Champeign. lllinois stezo

 

BNVN

 

31V!)

 

 

Comment a 1970 by the institute tot Personality and Ability Testing in: P 0 Be- the Chamfliort itimois 61820 An rights reserved
Printed m the Uri-tea States at Amer-ca Cat No It J58

 

120

 

121

 

 

IMAGE-CA - Imagery Scoring Sheet

CANCER CELLS
1. VIVIOMOS ....................
2. Activity ......................

3. Strength .....................

WHITE BLOOD CELLS (Immune
System)

4. Vivldness ....................
5. Activity ......................

6. Numeroslty (relative to Cancer
Cells) .......................

7. Size (relative to Cancer Cells) . . .

8. Strength .....................

CircietnenemMyoureeleeatdeecrieeetheimgery.

1

“'7 006m!

1

m OCH”

1

very strong

1
very unclear

1

not ICING

1

menyrhoreCa

theh WBC
1

Ce much larger

than wec

1
quit. I'll

2

W'hll unclear

2

on". ”I".

2

quite strong

WHO! unclear

2

mum

teer more Ca
than WBC

2
Ceeehtewhatterger

2
moderately welt

TREATMENT (Circle “3" it patient is not receiving treatment)

9. Vividness ....................

10. Etiectiveness .................

GENERAL

11 . How Symbolistic is visualization
vs. How Concrete .............

Overall Strength of Imagery
vs. Weakness .................

12.

13. Estimated regularity ...........

14. In your opinion, how is this type

of imagery related to short-term

very unclear.
cohlueed

1

1
m 'KTUCI .
concrete

1
verywedt

1
Mt imaging

1

2

eomowhet uncle"

2

not at all ettecttve moderately matter:-

mooereteiy factual.
concrete

2

quite weal

IMMMI

mmnly vrvid

3

MW.’ “I".

3

moderately strong

3
moderatety clear

mm." “I I"

3
abouttheeelhe
WICICC

3
Ca and WIC
eootit same

3
somewhat strong

moderately Clear

3
menu emc-
tive

3

untied symboliettcl
lactuel

3
moderately regular

3

dim W .......... COMM some some emanation ”new
GM mum

beseech the information you have available.

4

Quit. etVId

4

WM! Oct!”

4

moderately weeh

4

matte vivid

4

Quite active

te- more WIC

4
wec intte larger

4

gene strong

4

quote vivid

4
quite etteettve

moderately
aymooitettc

quite strong

high level or
consistency

5

maximumty were

5

00‘ II I" “It”

5

mute eieel

5

meermumty wind

5

m OCH"

5

many more WBC
than Ca

5
wec tnucn target
than Ce

5
very strong

5

m MIC.
CW

Monty "m".

highly eymboliettc

very some. strong

5

entremeiy treguent

M ”MD"

 

122

 

 

IMAG E-CA - Interview Record ”‘9‘ 3

Cancer
1. Describe how your cancer cells look in your mind‘s eye.

2. Do you see the cancer cells moving around? It so. how? When?

3. How strong (tough) do you think your cells are? (Score on strength described or imputed to symbol
chosen).

White Blood Cells [NBC]
4. Describe your WBC. (Score on vividness. clarity. continuity of description).

5. Do you see your WBC moving? It so. how? Where? (Score on activity or potential activity of symbol).

6. Do you see more cancer or more WBC? (Scoring on obvious response).

 

 

123

 

 

7. How big are your cancer cells? Your white blood oells?‘ (Score on relative difference with “5” indicating
WBC significantly larger).

8. How do the WBC fight disease in your body? How well do you see the WBC as doing their job? (Score on
strength or effectiveness).

Treatment
9. How does your treatment work to rid your body of disease? (Score on clarity and vividness).

10. How well does yOur treatment wont to kill off disease? (Score on effectiveness described).
Miscellaneous Response

11 . (Score on symbolism vs. concretlon).

12. (Score on weak vs. strong).

13. How many times edey do you think about (or image) your cancer? (Record response).

14. (Score imagery on basis of how you would predict It related to disease from a clinical standpoint. i.e..
“5" would indicate It predicted complete recovery. a “1 " would predict a poor prognosis or death).

‘ Patient may be cohlused on difference between cancer cell and tumor If so. some explanation or recording may be resulted.

 

 

124

 

 

 

 

 

 

 

L

' flats: For individuals with relatively little experience
than 50 administrations). omission of Dimension 14 Is advised.
tableontheleftofthispagsshouldbeussd.
procedures. see pp. M. Imagery of Cancer: A

Achterberg s Lawlis. 197s.

 

 

 

IMAG E-CA - Summary Data *
(1) (2) m (4)
W
Olmettalen SearexVIel'tt I Bears
1 x 1
2 __ x 1
3 _____ x 3
4 __ x 3
5 ___._ x 4
6 __ x 1 __
7 __ x 3
8 __ x 4
9 .____ x 2
10 __ x 3
1‘ — I‘ ‘ WeightedSum
12 __ x 8 __ Without Dimension 14
,3 i, 1 ”:(mCqumneSeendssbsIow)
14 __ x is ”SW-09010630")
Witholmensionte
(sesColumnsSbandebbelow)
StanenveralshTable Standorivenlenfeble
FerUserthOnlyflDtmenstene FortlsvethAlIMDln-enslena
(omitting clinical judgment. Dimension 14)
(5e) (lei (ID) (“i
Sum Stan Sum Stan
1650rgreeter.... 10 247orgrester.... 10
I
153-182 ......... s E‘“""”""°" 22am ......... s E‘““"" ""°"’
144.152 ......... 8 1713-2” ......... 8
134-143 ......... 7 °°°°'""°°" tee-212 ......... 7 °°°°""'°"’
125-133 ......... 6 178-194 ......... 6
' A
115-124 ......... 5 ""”"""°"’ 181-177 ......... s ""°"""°"’
toe-114 ......... 4 Lsssthaneveregs 144-160 --------- 4 Lmthsflm
96-105 ......... 3 imagery 127-143 ......... 3 imagery
87.95 ......... 2 , 110-13 ......... 2
Immense ..... 1 '°°'""‘°"’ lessthantOB.... 1 ”am-"9'”

using the IMAGE-CA drawing technique (less

Therefore. the sten conversion
For a more detailed explanation of scoring
n salvation tool for the process of disease.

 

 

 

APPENDIX F

Participants' Informed Consent Agreements

APPENDIX F

Participants’ Informed Consent Agreements

Husband's Informed Consent to Participate in Research Study

1, . the undersigned person,
hereby knowingly and voluntarily consent to participate in learning guided
imagery with my wife over a fifteen day period of time. I understand that the
guided imagery included in this investigation is under careful study and that
no benefits for its use can be promised, and the researcher in no way con-
dones its use in place of standard medical treatment.

My participation in this study will occur in three two-hour educational or
imagery sessions spaced one week apart. I understand that interviews about
my specific images and their pictorial representations will be conducted and
videotaped. In all resulting discussions or publications my identity will
remain confidential; on all forms and in any specific descriptions I under-
stand that I will be referred to by coded number. I consent to completing a
standardized 32-item scale about my marriage. It has been explained that I
will bear no cost for any of the sessions. I also understand that I may with-
draw from this study at any time without penalty.

The above paragraphs have been explained to me and I have had an oppor-
tunity to ask any questions. I certify that I understand the contents of this
form.

 

Signature of Participant

 

Signature of Witness

 

Date

125

126
Wife's Informed Consent to Participate in the Research Study

1, . the undersigned person, hereby
knowingly and voluntarily consent to learning guided imagery with my
husband as well as to five blood draws over a fifteen day period of time (Day 1
-- one 12.5 cc draw; Days 8 & 15 -- two 12.5 cc draws). I understand that the
blood will be drawn by a registered nurse, medical technologist, or licensed
phlebotomist, and analyzed at the Immunology Laboratory at Sparrow
Hospital. I understand that my physician has given medical approval for me
to participate in these procedures. I realize that occasionally there is some
discomfort associated with insertion of a needle, and that infrequently a
bruise may appear at the site of the draw. I further understand that the
guided imagery included in this investigation is under careful study and no
benefits from its use can be promised, and the researcher in no way condones
its use in place of standard medical treatment.

My participation in this study will occur in three two-hour educational or
imagery sessions spaced one week apart. I understand that interviews about
my specific images and their pictorial representations will be conducted and
videotaped. In all resulting discussions or publications my identity will
remain confidential; on all forms and in any specific descriptions I
understand that I will be referred to by coded number. I consent to
completing a standardized 32-item scale about my marriage. It has been
explained that I will bear no cost for any of the sessions, blood draws, or blood
test analyses. I also understand that I may withdraw from this study at any
time without penalty.

The above paragraphs have been explained to me and I have had an opportu -
nity to ask any questions. I certify that I understand the contents of this
form.

 

Signature of Participant

 

Signature of Witness

 

Date

APPENDDI G

Solicitation Letter to Potential Subjects

APPENDIX G

Solicitation Letter to Potential Subjects

September, 1994

Thank you for considering participation in this research study about guided
imagery and breast cancer. During the past year you have probably under-
gone a series of exhausting treatments. I am interested in the role that
guided imagery may have as an adjunctive therapy for cancer patients. As
you read further you will see that I am asking for a lot from you and that you
may not personally benefit from your participation in this study. However, it
is my belief that the information obtained with your help will be useful to
other women who will be diagnosed with breast cancer in the future.

In order to participate in this investigation you must be married and living
with your husband, have had premenopausal breast cancer on one side only,
and completed the course of medical treatment prescribed by your physi-
cian(s). Both husband and wife are asked to attend three one- to two-hour
sessions, spaced one week apart and at the same time each week. You also
must be willing to have five blood tests (one blood draw at the first session;
and two blood draws one hour apart at both the second and third sessions).
Your physician will need to verify that five blood draws within fifteen days
are not contraindicated. (Husbands will not have blood tests.)

During the three sessions you and your husband will receive information
about the body's defenses and will learn a guided imagery procedure.
Following the imagery sessions you will be asked to make a drawing of your
imagery. You will also participate in a tape-recorded interview about each of
your specific images.

For the purpose of gathering background information, you would also have to
agree to the release of specific medical information about your diagnosis and
course of medical treatment. At each of the three sessions the wife will be
asked to complete a brief questionnaire about recent illnesses, use of drugs,
and life changes. Both husband and wife will also be asked to complete a 32-
item scale about your marriage. Your confidentiality will be protected and all
oral or written discussion of the study results will refer to participants only
by an identification number.

127

128

There will be no cost to you for any of the blood tests or the informational and
imagery sessions. Since this research is rather expensive, I do ask that you
and your husband agree to participate only after careful thought and consid-
eration. I will meet with you to show you the exact medical information
requested, to outline the procedure of the three sessions, and to answer any
questions that either of you may wish to ask.

I encourage you to contact me for more information if you believe you might
be interested. There will be no pressure for you to participate for several
reasons. First, you have been through a lot already and second, I only wish to
solicit committed participants who will follow through with the three sessions
which include five blood draws. Due to the expensive nature of this type of
study, it will be important for participants to feel capable and willing to
attend all three sessions at the agreed upon times. Following the completion
of the study, you will have the opportunity to learn about the findings. I'm
looking forward to hearing from you if you would like more information.

Cordially,

Ellen Leroi, M.A.

Department of Family and Child Ecology
Michigan State University

(517) 349-1027

APPENDIX H

UCRIHS

APPENDIX H

 

 

UNIVERSITYCOMMITTEE ON RESEARCH INVOLVING
HUMAN OR ANIMAL SUBJECTS

 

The Graduate School
Michigan State University
118 Linton Hall

University and federal policies and procedures require that all research involving human or
animal subjects receive prior approval from the appropriate review board. (See Faculty
Handbook, p. 116-117 and the Academic Programs book, p. 60.)

HUMAN SUBJECTS
Does the thesis or dissertation you are submitting include research involving htunan
subjects or materials of human origin? (Research involving human subjects includes
surveys and telephone interviews used for research; materials of human origin include
human blood and /or tissue.) .
Yes 8 No D

If yes, indicate UCRIHS log ntunber for the approved protocol and attach the UCRIHS
approval letter for that protocol to this form.

 

 

 

UCRIHS Log Number: 94 - fl 7 7

 

 

ANIMAL SUBJECTS

Does the thesis or dissertation you are proposing to submit include research involving
vertebrate animals in any way?
Yes D No

If yes, and an animal use form was submitted to the All-University C ommittcc on Animal
Use and Care (AUCAUC). please list the approval number below and attach a copy of the
AUCAUC approval letter to this form.

 

 

AUF Number:

 

 

If yes, but your project did not need an animal use form. provide a copy of the letter from
the AUCAUC which cites the relevant exclusionary policy.

 

 

 

 

EH24) Leroi Donald Melcer,, PM.)
Student's Name (print) . Major Professor's Name (print)
Student's Signature Major Professor's Signature

129

MICHIGAN STATE 130

U TV I \/ E ii 5 l 1' Y

June 6, 1995

 

To: Ellen Leroi
4431 Elmwood Drive
Okemos, Mi 48864

RE: IRE}: 94-097
TITLE: EFFICACY OF INDIVIDUAL AND CONJOINT GUIDED
IMAGERY WITH BREAST CANCER PATIENTS

REVISION REQUESTED: N/A
CATEGORY: FULL REVIEW
APPROVAL DATE: 06/05/95

The University Committee on Research Involving Human Subjects'(UCRIHS)
review of this project is complete. I am pleased to adVise that the
rights and welfare of the human subjects appear to be adequately
protected and methods to obtain informed consent are appropriate.
gerefore, the UCRIHS approved this project and any revisions listed
a ove.

RENEWAL: UCRIHS approval is valid for one calendar year, beginning with
the approval date shown above. Investigators planning to
continue a project be ond one year must use the green renewal
form (enclosed with t e original agproval letter or when a
project is renewed) to seek u date certification. There is a
maXimum of four such expedite renewals ossible. Investigators
wishing to continue a project beyond tha time need to submit it
again or complete reView.

REVISIONS: UCRIHS must review any changes in procedures involving human
subjects, rior to initiation of t e change. If this is done at
the time o renewal, please use the green renewal form. To
revise an approved protocol at any other time during the year,
send your written request to the CRIHS Chair, requesting reVised
approval and referencing the project's IRB # and title. Include
in our request a description of the change and any revised
ins ruments, consent forms or advertisements that are applicable.

PROBLEMS]

CHANGES: Should either of the followin arise during the course of the
work, investigators must noti UCRIHS promptly: (1) problems
(unexpected Side effects, comp aints, e c.) invOIVing uman
subjects_or (2) changes in the research environment or new
information indicating greater risk to the human sub'ects than

 

OflmEW' 7 existed when the protocol was previously reviewed an approved.
RESEARCH
AND If we can be of any future help, lease do not hesitate to contact us
GRADUATE at (517)355-2180 or FAX (517)432- 171.
STUDIES ,
Sincerely,

   
 
 

Jniversity Committee
Research Involving \
Human Subjects

(UCRIHS) David E . tag-1..
Michigan Slate University UCRIHS Chal '

232Ammmammn&MMm DEW:kaa/lcp

East Lansing. Michigan
433244045 cc : Donald Melcer

 
 
  

  

517/355-2180
FAX: 517/432-1171

Tne Michigan State Universiiy
i'DEA is Insular/anal DIVEVSIW.
Excerlence m Action

MS U IS an affirmative-action.

an: aunnnnm 1mm incmnlinn

APPENDDI I

Medical Release and Information Forms

APPENDDI I

Medical Release and Information Forms

Participant's Authorization for Disclosure of Medical Records

Patient Name

 

Birthdate

 

Information to be requested from:

Name

 

Address

 

 

 

Information to be released to:

Ellen Leroi, M.A.

Department of Family and Child Ecology
Unit 4 Paolucci Building

Michigan State University

East Lansing, MI 48824

I hereby consent to the disclosure of information contained in my
medical record, including:

Date of breast cancer diagnosis:

 

Type of surgical treatment (please check)
Lumpectomy date:

 

Mastectomy date:

 

131

132
Number of lymph nodes removed:

Breast reconstruction date:

 

Type of systemic treatment (please check)

Chemotherapy
number of treatments:

treatment dates:

 

oncologist's name:

 

combination and amounts of chemical treatment:

 

 

 

 

total length of time:

 

Radiation
number of treatments:

treatment dates:

 

 

radiology oncologist's name:

 

type and amount of radiation:

 

 

 

total length of time:

 

Autologous Bone Marrow Transplant (ABMT)
date of procedure:

 

physician's name:

 

Hormonal therapy
name of drug:

 

dose:
length of time used:
prescribing physician:

 

 

 

Pathology (please check)
invasive

133
non-invasive
type of cancer cells:

 

stage number:

size of tumor:

spread to lymph nodes? __ how many?
presence of metastasis? __ Please describe the extent:

 

 

 

 

estrogen-receptor negative
estrogen-receptor positive

Immune system function

Please check if the patient shows evidence of the human immuno—
deficiency virus (HIV), acquired immunodeficiency syndrome (AIDS) or AIDS
related complex (ARC), information made confidential by Public Act 488 of
1988 as amended by Public Act 174 of the State of Michigan Health Code.

 

Purpose and Need for such Disclosure:
Participation in a Michigan State University doctoral dissertation
research study.

I understand that I may revoke this authorization at any time and that this
authorization pertains to fulfillment of the stated purpose of a research
study. This authorization will automatically expire after six months from the
date of signature.

I have read the above, and acknowledge that I am familiar with and fully
understand the terms of this authorization.

Date: Participant signature:

 

Date: Witnessed by:

 

134

Participant Background and History

Name:

 

Address:

 

 

Date of Birth:

 

Home Phone: Work Phone:

 

Husband's Name:

 

His Work Phone:

 

His Date of Birth:

 

Month and Year of Breast Cancer Diagnosis:

 

Afl‘ected Side (left or right):

 

Please summarize in your own words what has happened (surgery, chemo-
therapy, radiation, support groups, individual, couple or group therapy) from
the time of breast cancer diagnosis until now.

135

Please briefly describe your knowledge and beliefs about guided imagery and
any previous experiences that you may have had using this technique.

Wife's Reply:

Husband's reply:

136

Physician Approval Form

has consulted with me about
participating in a research study that requires five venipunctures within a
two week period.

 

As the physician of Ms. , I see no contraindications in
drawing 12.5 cm3 of blood at each instance, according to the following
schedule: Day 1, one venipuncture

Day 8, two venipunctures, about one hour apart

Day 15, two venipunctures, about one hour apart

 

Physician Signature

 

Physician Name (printed)

 

Date

137
Participant Information Update

 

 

Session Qne
Name: Today's Date:
1. Please list all medications (prescribed or over-the-counter) that you have

taken during the past 30 days. [Name of medication, how much, how
often, number of days taken]

Have you smoked marijuana or taken any street drugs during the past 30
days? Ifyes, list all drugs, and how much and how often used.

 

Have you been ill during the past 30 days?
conditions.

If yes, describe the

Have you visited a physician during the past 30 days?
what purpose?

If yes, for

. Have you received any medical treatment during the past 30 days?

Ifyes, describe the treatment.

Have there been any interpersonal or other changes during the past
month? (e.g., ending or beginning a job; death of a family member, friend
or pet; conflict with co-workers, family or friends; birth of a child; etc.)
Please explain.

. Please indicate below anything else that has happened during the past 30

days that you think might be important to share.

138
Participant Information Update

 

 

fiessign TWQ
Name: Today's Date:
1. Please list all medications (prescribed or over-the-counter) that you have

taken since our meeting last week. [Name of medication, how much, how
often, number of days taken]

. Have you smoked marijuana or taken any street drugs during the past

week? Ifyes, list all drugs, and how much and how often used.

 

. Have you been ill during the past week? If yes, describe the
conditions.
Have you visited a physician during the past 7 days? If yes, for
what purpose?

. Have you received any medical treatment since our last session?

If yes, describe the treatment.

Have there been any interpersonal or other changes during the past
week? (e.g., ending or beginning a job; death of a family member, friend
or pet; conflict with co-workers, family or friends; birth of a child; etc.)
Please explain.

. Please indicate below anything else that has happened during the past

week that you think might be important to share.

139
Participant Information Update

Session Three

Name: Today's Date:

1.

 

Please list all medications (prescribed or over-the-counter) that you have
taken since our meeting last week. [Name of medication, how much, how
often, number of days taken]

. Have you smoked marijuana or taken any street drugs during the past

week? If yes, list all drugs, and how much and how often used.

Have you been ill during the past week?
conditions.

If yes, describe the

Have you visited a physician during the past 7 days?
what purpose?

If yes, for

. Have you received any medical treatment since our last session?

If yes, describe the treatment.

Have there been any interpersonal or other changes during the past
week? (e.g., ending or beginning a job; death of a family member, friend
or pet; conflict with co-workers, family or friends; birth of a child; etc.)
Please explain.

Please indicate below anything else that has happened during the past
week that you think might be important to share.

APPENDDI J

Wives’ IMAGE-CA Drawings

APPENDDI J
Wives’ IMAGE-CA Drawings

s . V'
‘ 5'
{-
*‘n
i .
'r |
1’ “"\.A i .
' ' l.
‘ l
l". f f
9
.-‘ (1'4
' -v,. ..
j l -

 

1. Subject 1, image alone, 12-10-94

140

141

 

Subject 1, image together, 12-17-94

142

 

 

 

Subject 2, image alone, 12-18-94

143

 

Subject 2, image together, 12-11-94

 

  

144

‘2 *x
I c H
(L) k . (
“I“ M ’r‘ ’-l
"' t".
1"
"i

 

eta-wus—mmm will“ , a 4
.21:,r.'.

Subject 3, image alone, 2-5-95

 

 

 

145

'11”.

lflyrfiwg‘fii‘l )'

_._‘ .

s. p .
”item.

' an cut-w“

 

Subject 3, image together, 2-12-95

146

 

 

Subject 4, image alone, 5-21-95

, ~
.. fl‘na‘ ‘

i
’ l

'm*m"el‘:u:~m~

‘.

147

 

Subject 4, image together, 4-30-95

148

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Subject 5, image alone, 8-20-95

149

 
      
  
 
   
 
 
 
 

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-"/ l 3 “it” ”’7. r f .4511:
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«taffeta?

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Subject 5, image together, 8-27-95

150

 

Subject 6, image alone, 1-21-96, drawing 1

151

 

Subject 6, image alone, 1-21-96, drawing 2

152

 

Subject 6, image alone, 1-21-96, drawing 3

153

 

if, if)
I ‘ .~

Subject 6, image alone, 1-21-96, drawing 4

154

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Subject 6, image alone, 1-21-96, drawing 5

155

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Subject 6, image together, 1-14-96, drawing 2

APPENDIX K

LogXact Output

APPENDIX K
LogXact Output

File: WBCALONEDAT

Model: RESPONSE=ALONETOG+IMAGECA
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12

#Groups: 12

Deviance : 9.8659 on 9 df

Tests ( 1 df) : <ALONETOG>

Type of Test Statistic P-value P-mid

Likelihood Ratio 0.5110 0.4747 NA

Wald 0.4279 0.5130 NA

Scores 0.4520 0.5014 NA

Exact (Conditional Scores) 1.0000 1.0000 0.7500

Tests ( 1 df) : <IMAGECA>

Type of Test Statistic P-value P-mid

Likelihood Ratio 6.0901 0.0136 NA

Wald 2.8053 0.0940 NA

Scores 4.8193 0.0281 NA

Exact (Conditional Scores) 4.0161 0.0400 0.0383

Tests ( 2 df) : <ALONETOG,IMAGECA>°

Type of Test Statistic P-value P-mid

Likelihood Ratio 6.4348 0.0401 NA

Wald 3.2662 0.1953 NA

Scores 5.1779 0.0751 NA

Exact (Conditional Scores) 4.7464 0.0682 0.0676

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED

ALONETOG Asymptotic -1.4360 2.1953 -INF 2.1749 0.2565
Exact 0.0002 <<MUE -INF 2.9452 0.5000

IMAGECA Asymptotic -0.0682 0.0407 -INF -0.0012 0.0470
Exact -0.0543 NA -INF -0.0076 0.0200

CONST Asymptotic 12.0648 7.6672 -0.5467 +1NF 0.0578

Exact

 

 

158

159

File: WBCALONEDAT

Model: RESPONSE=ALONETOG+DAS
Strat Var: <Unstratified>

Weight: <Unweighted>

#Obs: 12

#Groups: 4

Deviance 4.7271 on ldf

~~----‘------------------------------‘------—-‘-~‘----—'---------—---‘-------

Tests ( 1 df) : <ALONETOG>

 

 

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.4511 0.5018 NA
Wald 0.4306 0.5117 NA
Scores 0.4444 0.5050 NA
Exact (Conditional Scores) 0.3684 1.0000 0.8036
Tests ( 1 df) : <DAS>

Type of Test Statistic P-value P-mid
Likelihood Ratio 2.9110 0.0880 NA
Wald 2.4312 0.1189 NA
Scores 2.8235 0.0929 NA
Exact (Conditional Scores) 2.3529 0.2400 0.1667
Tests ( 2 df) : <ALONETOG,DAS>

Type of Test Statistic P-value P- mid
Likelihood Ratio 3.2557 0.1963 NA
Wald 2.5360 0.2814 NA
Scores 3.0857 0.2138 NA
Exact (Conditional Scores) 2.8286 0.4242 0.3965
VARIABLE INFEREN CE < ------- PARAMETER ESTIMATION -------- > P-VALUE

TYPE BETA

ALONETOG Asymptotic 0.9163

Exact 0.7631 NA -1.927 4 +INF
DAS Asymptotic -2.3026 1.4767 -INF 0.1264
Exact -1.8409 NA -INF 0.7027

CONST Asymptotic 0.6931 1.2935 -1.4344 +INF

Exact

1.3964 -1.3806 +INF

SE(BETA) 95.0% (1-sided)BOUND ONE SIDED

0.2559
0.5000

0.0595
0.1600

0.2960

 

 

160

File: WBCALONEDAT

Model: RESPONSE=ALONETOG+DASII
Strat Var: <Unstratified>

Weight: <Unweighted>

#Obs: 12

#Groups: 12

Deviance : 14.4652 on 9 df

Ont-------------n-----n-“--_-----n---—------n-uncn‘m-QuQ-nnnu-‘un-----------

Tests ( 1 df) : <ALONETOG>

 

 

Type of Test Statistic P-value
Likelihood Ratio 0.3915 0.5315
Wald 0.3812 0.5369
Scores 0.3881 0.5333
Exact (Conditional Scores) 0.3333 1.0000
Tests ( 1 df) : <DASII>
Type of Test Statistic P—value
Likelihood Ratio 1.4908 0.2221
Wald 1.3110 0.2522
Scores 1.4351 0.2309
Exact (Conditional Scores) 1.1959 0.3000
Tests ( 2 df) : <ALONETOG,DAS#>
Type of Test Statistic P—value
Likelihood Ratio 1.8355 0.3994
Wald 1.5364 0.4639
Scores 1.7370 0.4196
Exact (Conditional Scores) 1.5922 0.4697
VARIABLE INFERENCE < ....... PARAMETER ESTIMATION -------- >
TYPE BETA SE(BETA) 95.0% (1-sided)BOUND
ALONETOG Asymptotic 0.7900 1.2795 -1.3146 +INF
Exact 0.6932 NA -1.8549 +INF
DAS# Asymptotic -0.0516 0.0451 -INF 0.0225
Exact 004% NA -lNF 0.0230
CONST Asymptotic 4.7783 4.7982 -3.1140 +INF

Exact

P-mid
NA
NA
NA

‘ 0.8125

P~ mid
NA
NA
NA
0.2950

P-mid
NA
NA
NA
0.4678

P-VALUE
ONE SIDED

0.2685
0.5000

0.1261
0.1567

0.1597

 

 

161

File: LYMPHSAL.DAT

Model: RESPONSE=ALONETOG+IMAGECA
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12

#Groups: 12

Deviance : 7.7189 on 9 df

Tests ( 1 df) : <ALONETOG>

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.0054 0.9416 NA

Wald 0.0054 0.9413 NA
Scores 0.0054 0.9413 NA
Exact (Conditional Scores) DEGENERATE ? ?

Tests ( 1 df) : <IMAGECA>

Type of Test Statistic P-value P-mid
Likelihood Ratio 7.5574 0.0060 NA

Wald 3.5698 0.0588 NA
Scores 6.1836 0.0129 NA
Exact (Conditional Scores) 5.1530 0.0133 0.0111
Tests ( 2 df) : <ALONETOG,IMAGECA>

Type of Test Statistic P-value P- mid
Likelihood Ratio 8.9166 0.0116 NA

Wald 3.9385 0.1396 NA
Scores 6.8298 0.0329 NA
Exact (Conditional Scores) 6.2607 0.0173 0.0168
VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE

TYPE BETA SE(BETA) 95.0% (l-sided)BOUND ONE SIDED
ALONETOG Asymptotic 0.1510 2.0519 -3.2241 +INF 0.4707
Exact Degenerate NA ? ? ?
IMAGECA Asymptotic 0077 5 0.0410 -INF -0.0100 0.0294
Exact ~0.0556 NA -INF -0.0106 0.0133
CONST Asymptotic 13.7900 7.7344 1.0681 +INF 0.0373

Exact

 

 

162

File: LYMPHSAL.DAT

Model: RESPONSE=ALONETOG+DAS
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12
#Groups: 4
Deviance : 0.7338 on 1 df
Tests ( 1 df) : <ALONETOG>
Type of Test Statistic P-value P-mid
Likelihood Ratio 1.3592 0.2437 NA
Wald 1.2812 0.2577 NA
Scores 1.3333 0.2482 NA
Exact (Conditional Scores) 1.1053 0.5810 0.4607
Tests ( 1 df) : <DAS>
Type of Test Statistic P-value P-mid
Likelihood Ratio 0.0000 1.0000 NA
Wald 0.0000 1.0000 NA
Scores 0.0000 1.0000 NA
Exact (Conditional Scores) 0.0000 1.0000 0.7756
Tests ( 2 df) : <ALONETOG,DAS>
Type of Test Statistic P-value P-mid
Likelihood Ratio 1.3592 0.5068 NA
Wald 1.2812 0.5270 NA
Scores 1.3333 0.5134 NA
Exact (Conditional Scores) 1.2222 0.8095 0.7549
VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0% (l-sided)BOUND ONE SIDED
ALONETOG Asymptotic 1.3863 1.2247 -0.6282 +INF 0.1288
Exact 1.1247 NA -1.0956 +INF 0.2905
DAS Asymptotic 0.0000 1.2990 -2.1367 +INF 0.5000
Exact 0.0000 NA -2.6498 +INF 0.7244
CONST Asymptotic -0.6931 1.2247 -INF 1.3214 0.2857

Exact

 

 

163

File: LYMPHSAL.DAT

Model: RESPONSE=ALONETOG+DASII
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12
#Groups: 12
Deviance : 14.2804 on 9 df
Tests ( 1 df) : <ALONETOG>
Type of Test Statistic P-value P-mid
Likelihood Ratio 1.4759 0.2244 NA
Wald 1.3527 0.2448 NA
Scores 1.4360 0.2308 NA
Exact (Conditional Scores) 2.0000 0.5000 0.3750
Tests ( 1 df) : <DASII>
Type of Test Statistic P-value P-mid
Likelihood Ratio 0.9959 0.3183 NA
Wald 0.9051 0.3414 NA
Scores 0.9651 0.3259 NA
Exact (Conditional Scores) 0.8043 0.3911 0.3867
Tests ( 2 df) : <ALONETOG,DAS#>
Type of Test Statistic P-value P-mid
Likelihood Ratio 2.3551 0.3080 NA
Wald 1.8803 0.3906 NA
Scores 2.1912 0.3343 NA
Exact (Conditional Scores) 2.0086 0.3485 0.3474
VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED
ALONETOG Asymptotic 1.5190 1.3061 -0.6293 +INF 0.1224
Exact 0.8813 <<MUE -1.2456 +INF 0.2500
DAS# Asymptotic -0.0429 0.0451 «INF 0.0312 0.1707
Exact -0.0355 NA -INF 0.0293 0.1956
CONST Asymptotic 3.8650 4.8156 -4.0560 +INF 0.2111

Exact

 

 

164

 

 

File: CD3ALONE.DAT
Model: RESPONSE=ALONETOG+IMAGECA
Strat Var: <Unstratified>
Weight: <Unweighted>
#Obs: 12
#Groups: 12
Deviance 9.7278 on 9 df
Tests ( 1 df) : <ALONETOG>
Type of Test Statistic P—value P-mid
Likelihood Ratio 0.5422 0.4615 NA
Wald 0.4498 0.5024 NA
Scores 0.4768 0.4899 NA
Exact (Conditional Scores) DEGENERATE ? ?
Tests ( 1 df) : <IMAGECA>
Type of Test Statistic P-value P-mid
Likelihood Ratio 6.2282 0.0126 NA
Wald 2.8078 0.0938 NA
Scores 4.8987 0.0269 NA
Exact (Conditional Scores) 4.0822 0.0367 0.0350
Tests ( 2 df) : <ALONETOG,IMAGECA>
Type of Test Statistic P-value P- mid
Likelihood Ratio 6.5729 0.0374 NA
Wald 3.2729 0.1947 NA
Scores 5.2575 0.0722 NA
Exact (Conditional Scores) 4.8194 0.0657 0.0650
VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED
ALONETOG Asymptotic -1.4990 2.2351 2.1775 0.2512
Exact Degenerate NA ?
IMAGECA Asymptotic -0.0699 0.0417 -0.0013 0.0469
Exact -0.0555 NA -0.0092 0.0167
CONST Asymptotic 12.3815 7.8539 -0.5370 +INF 0.0575

Exact

 

 

165

File: CD3ALONE.DAT
Model: RESPONSE=ALONETOG+DAS

Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12

#Groups: 4

Deviance : 0.1913 on 1 df

Tests ( 1 df) : <ALONETOG>

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.3498 0.5542 NA
Wald 0.3438 0.5576 NA
Scores 0.3478 0.5553 NA
Exact (Conditional Scores) 0.2877 1.0000 0.8155
Tests ( 1 df) : <DAS>

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.1756 0.6752 NA
Wald 0.1751 0.6757 NA
Scores 0.1765 0.6744 NA
Exact (Conditional Scores) 0.1471 1.0000 0.7933
Tests ( 2 df) : <ALONETOG,DAS>

Type of Test Statistic P-value P- mid
Likelihood Ratio 0.5203 0.7709 NA
Wald 0.4988 0.7793 NA
Scores 0.5143 0.7733 NA
Exact (Conditional Scores) 0.4714 1.0000 0.9217
VARIABLE INFERENCE < ------- PARAMETER ESTMATION -------- > P-VALUE

SE(BETA) 95.0% (l-sided)BOUND ONE SIDED

ALONETOG Asymptotic 0.7037 1.2002 ~1.2704 +INF 0.2788
0.5786 NA -1.6932 +INF 0.5000

DAS Asymptotic -0.5264 1.2582 1.5431 0.3378
-0.4366 NA 2.1018 0.5733

CONST Asymptotic -0.3519 1.1796 1.5884 0.3827

 

 

166

Model: RESPONSE=ALONETOG+DAS#
Strat Var: <Unstratified>

Weight: <Unweighted>

#Obs: 12

#Groups: 12

Deviance : 15.2374 on 9df

Tests ( 1 df) : <ALONETOG>

File: CD3ALONE.DAT

 

 

Type of Test Statistic P-value P-mid

Likelihood Ratio 0.3662 0.5451 NA

Wald 0.3586 0.5493 NA

Scores 0.3637 0.5465 NA

Exact (Conditional Scores) 0.3333 1.0000 0.8125

Tests ( 1 df) : <DAS#>

Type of Test Statistic P-value P-mid

Likelihood Ratio 0.7185 0.3966 NA

Wald 0.6772 0.4106 NA

Scores 0.7065 0.4006 NA

Exact (Conditional Scores) 0.5888 0.4933 0.4883

Tests ( 2 df) : <ALONETOG,DAS#>

Type of Test Statistic P-value P-mid

Likelihood Ratio 1.0632 0.5877 NA

Wald 0.9578 0.6195 NA

Scores 1.0292 0.5977 NA

Exact (Conditional Scores) 0.9434 0.6162 0.6143

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE

TYPE BETA SE(BETA) 95.0% (l-sided)BOUND ONE SIDED
ALONETOG Asymptotic 0.7375 1.2315 -1.2882 +INF 0.2746
Exact 0.6932 NA -1.8549 +INF 0.5000
DAS# Asymptotic -0.0346 0.0421 -INF 0.0346 0.2053
Exact -0.0287 NA -INF 0.0343 0.2433

CONST Asymptotic 2.9962 4.5238 4.4448 +INF 0.2539

Exact

 

 

167

File: CD4ALONE.DAT
Model: RESPONSE=ALONETOG+IMAGECA
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12

#Groups: 12

Deviance : 7.7189 on 9 df

Tests ( 1 df) : <ALONETOG>

Type of Test Statistic P-value P-mid

Likelihood Ratio 0.0054 0.9416 NA

Wald 0.0054 0.9413 NA

Scores 0.0054 0.9413 NA

Exact (Conditional Scores) DEGENERATE ? ?

Tests ( 1 df) : <IMAGECA>

Type of Test Statistic P-value P-mid

Likelihood Ratio 7.557 4 0.0060 NA

Wald 3.5698 0.0588 NA

Scores 6.1836 0.0129 NA

Exact (Conditional Scores) 5.1530 0.0133 0.0111

Tests ( 2 df) : <ALONETOG,IMAGECA>

Type of Test Statistic P-value P- mid

Likelihood Ratio 8.9166 0.0116 NA

Wald 3.9385 0.1396 NA

Scores 6.8298 0.0329 NA

Exact (Conditional Scores) 6.2607 0.017 3 0.0168

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE

BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED

ALONETOG Asymptotic 0.1510 2.0519 -3.2241 +INF 0.47 07

Exact Degenerate NA ?

IMAGECA Asymptotic -0.0775 0.0410

-0.0556 NA

CONST Asymptotic 13.7900 7 .7344

-0.0100 0.0294
-0.0106 0.0133

+INF

0.0373

 

 

168

File: CD4ALONE.DAT

Model: RESPONSE=ALONETOG+DAS
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12
#Groups: 4
Deviance : 0.7 338 on 1 df
Tests ( 1 df) : <ALONETOG>
Type of Test Statistic P-value P—mid
Likelihood Ratio 1.3592 0.2437 NA
Wald 1.2812 0.257 7 NA
Scores 1.3333 0.2482 NA
Exact (Conditional Scores) 1.1053 0.5810 0.4607
Tests ( 1 df) : <DAS>
Type of Test Statistic P-value P-mid
Likelihood Ratio 0.0000 1.0000 NA
Wald 0.0000 1.0000 NA
Scores 0.0000 1.0000 NA
Exact (Conditional Scores) 0.0000 1.0000 0.7756
Tests ( 2 df) : <ALONETOG,DAS>
Type of Test Statistic P-value P-mid
Likelihood Ratio 1.3592 0.5068 NA
Wald 1.2812 0.5270 NA
Scores 1.3333 0.5134 NA
Exact (Conditional Scores) 1.2222 0.8095 0.7549
VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED
ALONETOG Asymptotic 1.3863 1.2247 -0.6282 +INF 0.1288
Exact 1.1247 NA -1.0956 +INF 0.2905
DAS Asymptotic 0.0000 1.2990 -2.1367 +INF 0.5000
Exact 0.0000 NA -2.6498 +INF 0.7244
CONST Asymptotic -0.6931 1.2247 -INF 1.3214 0.2857

Exact

 

 

169

File: CD4ALONE.DAT

Model: RESPONSE=ALONETOG+DAS»?
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12
#Groups: 12
Deviance : 14.2804 on 9 df
Tests ( 1 df) : <ALONETOG>
Type of Test Statistic P-value P-mid
Likelihood Ratio 1.47 59 0.2244 NA
Wald 1.3527 0.2448 NA
Scores 1.4360 0.2308 NA
Exact (Conditional Scores) 2.0000 0.5000 0.3750
Tests ( 1 df) : <DASII>
Type of Test Statistic P-value P-mid
Likelihood Ratio 0.9959 0.3183 NA
Wald 0.9051 0.3414 NA
Scores 0.9651 0.3259 NA
Exact (Conditional Scores) 0.8043 0.3911 0.3867
Tests ( 2 df) : <ALONETOG,DAS#>
Type of Test Statistic P-value P-mid
Likelihood Ratio 2.3551 0.3080 NA
Wald 1.8803 0.3906 NA
Scores 2.1912 0.3343 NA
Exact (Conditional Scores) 2.0086 0.3485 0.3474
VARIABLE INFERENCE < ------- PARAMETER ESTIIVIATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0% (l-sided)BOUND ONE SIDED
ALONETOG Asymptotic 1.5190 1.3061 -0.6293 +1NF 0.1224
Exact 0.8813 <<MUE -1.2456 +INF 0.2500
DAS/I Asymptotic -0.0429 0.0451 -INF 0.0312 0.1707
Exact -0.0355 NA -INF 0.0293 0.1956
CONST Asymptotic 3.8650 4.8156 -4.0560 +INF 0.2111

Exact

 

 

170

File: CD8ALONE.DAT
Model: RESPONSE=ALONETOG+IMAGECA
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12

#Groups: 12

Deviance : 4.6449 on 9 df

Tests ( 1 df) : <ALONETOG>

Type of Test Statistic P-value P-mid

Likelihood Ratio 5.6034 0.0179 NA

Wald 1.1947 0.2744 NA

Scores 2.1342 0.1440 NA

Exact (Conditional Scores) DEGENERATE ? ?

Tests( 1 df) : <IMAGECA>

Type of Test Statistic P-value P-mid

Likelihood Ratio 11.9907 0.0005 NA

Wald 1.2270 0.2680 NA

Scores 6.2652 0.0123 NA

Exact (Conditional Scores) 5.2210 0.0100 0.0088

Tests ( 2 df) : <ALONETOG,IMAGECA>

Type of Test Statistic P-value P- mid

Likelihood Ratio 11.9907 0.0025 NA

Wald 1.2710 0.5297 NA

Scores 6.2652 0.0436 NA

Exact (Conditional Scores) 5.7431 0.0368 0.0363

VARIABLE INFERENCE < ------- PARAMETER ESTIIWATION -------- > P-VALUE

TYPE BETA SE(BETA) 95.0% (l-sided)BOUND ONE SIDED
ALONETOG Asymptotic —50.3429 46.0584 -INF 25.4164 0.1372
Exact Degenerate NA ? ? ?
IMAGECA Asymptotic -1.017 9 0.9189 -INF 0.4936 0.1340
Exact -0.5523 NA -INF -0.0227 0.0050

CONST Asymptotic 203.7922 184.5096 -99.6990 +INF 0.1347

Exact

 

 

171

Model: RESPONSE=ALONETOG+DAS

Strat Var: <Unstratified>
Weight: <Unweighted>

#Obs: 12
#Groups: 4
Deviance

0.0000 on 1 df

File: CD8ALONE.DAT

Tests ( 1 df) : <ALONETOG>

Type of Test

Likelihood Ratio

Wald
Scores

Exact (Conditional Scores)

Statistic
0.0000
0.0000
0.0000
0.0000

Tests ( 1 df) : <DAS>

Type of Test

Likelihood Ratio

Wald
Scores

Exact (Conditional Scores)

Statistic
0.0000
0.0000
0.0000
0.0000

Tests ( 2 df) : <ALONETOG,DAS>

Type of Test

Likelihood Ratio

Wald
Scores

Exact (Conditional Scores)

Statistic
0.0000
0.0000
0.0000
0.0000

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- >
SE(BETA) 95.0% (l-sided)BOUND ONE SIDED

ALONETOG Asymptotic 0.0000
0.0000 NA

DAS Asymptotic

CONST Asymptotic

1.1547

0.0000 1.2247
0.0000 NA

0.0000 1.1547

-INF
-]NF

-INF

-INF

-INF

1.8993
2.2410

2.0145
2.4932

1.8993

P-mid
NA
NA
NA
0.7905

P-mid
NA
NA
NA
0.7800

P- mid
NA
NA
NA
0.9048

P-VALUE

0.5000
0.7095

0.5000
0.7200

0.5000

 

 

172

File: CD8ALONE.DAT

Model: RESPONSE=ALONETOG+DAS/I
Strat Var: <Unstratified>

Weight: <Unweighted>

#Obs: 12

#Groups: 12

Deviance : 16.3412 on 9 df

Tests ( 1 df) : <ALONETOG>

 

 

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.0000 1.0000 NA
Wald 0.0000 1.0000 NA
Scores 0.0000 1.0000 NA
Exact (Conditional Scores) 0.0000 1.0000 0.7 500
Tests ( 1 df) : <DAS#>
Type of Test Statistic P-value P-mid
Likelihood Ratio 0.2943 0.5875 NA
Wald 0.2872 0.5920 NA
Scores 0.2919 0.5890 NA
Exact (Conditional Scores) 0.2433 0.6200 0.6175
Tests ( 2 df) : <ALONETOG,DAS#>
Type of Test Statistic P-value P-mid
Likelihood Ratio 0.2943 0.8632 NA
Wald 0.287 2 0.8662 NA
Scores 0.2919 0.8642 NA
Exact (Conditional Scores) 0.2676 0.8355 0.8344
VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED
ALONETOG Asymptotic 0.0000 1.1690 -1.9228 +INF 0.5000
Exact 0.0000 NA -INF 3.6508 0.7 500
DAS# Asymptotic -0.0212 0.0395 -INF 0.0439 0.2960
Exact ~0.0177 NA -INF 0.0404 0.3100
CONST Asymptotic 2.2859 4.3464 -4.8633 +INF 0.2995

Exact

 

 

173

File: SEGSALONDAT
Model: RESPONSE=ALONETOG+IMAGECA
Strat Var: <Unstratified>
Weight: <Unweighted>
#Obs: 12
#Groups: 12

Deviance : 12.3473 on 9 df

Tests ( 1 df) : <ALONETOG>

 

 

Type of Test Statistic P-value P-mid

Likelihood Ratio 1.9076 0.1672 NA

Wald 1.7597 0.1847 NA

Scores 1.9885 0.1585 NA

Exact (Conditional Scores) 1.5000 0.6667 0.5000

Tests( 1 df) : <IMAGECA>

Type of Test Statistic P-value P-mid

Likelihood Ratio 0.6976 0.4036 NA

Wald 0.6592 0.4168 NA

Scores 0.7008 0.4025 NA

Exact (Conditional Scores) 0.5840 0.5333 0.5278

Tests ( 2 df) : <ALONETOG,IMAGECA>

Type of Test Statistic P-value P- mid

Likelihood Ratio 3.9533 0. 1385 NA

Wald 3.0221 0.2207 NA

Scores 3.6733 0.1594 NA

Exact (Conditional Scores) 3.3672 0.2020 0.2014

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0% (l-sided)BOUND ONE SIDED

ALONETOG Asymptotic 1.9477 1.4683 -0.467 4 +INF 0.0923
Exact 0.4810 <<MUE -1.3585 +INF 0.3333

IMAGECA Asymptotic -0.0166 0.0205 -1NF 0.0171 0.2084
Exact -0.0136 NA -1NF 0.0179 0.2556

CONST Asymptotic 1.5051 3.9408 -4.9770 +INF 0.3513

Exact

 

 

174

File: SEGSALONDAT
Model: RESPONSE=ALONETOG+DAS

Strat Var: <Unstratified>
Weight: <Unweighted>

#Obs: 12

#Groups: 4

Deviance : 2.7692 on 1 df

Tests ( 1 df) : <ALONETOG>

Type of Test Statistic P-value P-mid
Likelihood Ratio 3.3171 0.0686 NA
Wald 2.7212 0.0990 NA
Scores 3.1304 0.0768 NA
Exact (Conditional Scores) 2.5890 0.2619 0.2024
Tests ( 1 df) : <DAS>

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.2318 0.6302 NA
Wald 0.2267 0.6340 NA
Scores 0.2308 0.6310 NA
Exact (Conditional Scores) 0.1923 1.0000 0.7778
Tests ( 2 df) : <ALONETOG,DAS>

Type of Test Statistic P-value P-mid
Likelihood Ratio 3.4876 0.1749 NA
Wald 2.7619 0.2513 NA
Scores 3.2571 0.1962 NA
Exact (Conditional Scores) 2.9857 0.3131 0.2879

 

 

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED

ALONETOG Asymptotic 2.3589 1.4300 0.0068 +INF 0.0495

Exact 1.8615 NA -0.5332 +INF 0.1310
DAS Asymptotic -0.7002 1.4708 -INF 1.7190 0.3170
Exact -0.5824 NA -INF 2.3390 0.5778
CONST Asymptotic -1.1795 1.3789 -INF 1.0886 0.1962

Exact

r
‘—

175

Model: RESPONSE=ALONETOG+DAS#

Strat Var: <Unstratified>
Weight: <Unweighted>

#Obs: 12

#Groups: 12

Deviance

: 13.0283 on 9df

File: SEGSALONDAT

Tests ( 1 df) : <ALONETOG>

 

 

Type of Test Statistic P-value P-mid

Likelihood Ratio 3.2600 0.0710 NA

Wald 2.7195 0.0991 NA

Scores 3.0889 0.0788 NA

Exact (Conditional Scores) 1.9565 0.3500 0.2750

Tests ( 1 df) : <DASII>

Type of Test Statistic P-value P-mid

Likelihood Ratio 0.0166 0.8976 NA

Wald 0.0165 0.8977 NA

Scores 0.0166 0.8976 NA

Exact (Conditional Scores) 0.0138 0.9444 0.9111

Tests ( 2 df) : <ALONETOG,DAS#>

Type of Test Statistic P-value P- mid

Likelihood Ratio 3.2723 0.1947 NA

Wald 2.7229 0.2563 NA

Scores 3.0980 0.2125 NA

Exact (Conditional Scores) 2.8398 0.3359 0.3321

VARIABLE INFEREN CE < ------- PARAMETER ESTIMATION -------- > P-VALUE
BETA SE(BETA) 95.0% (l-sided)BOUND ONE SIDED

ALONETOG Asymptotic 2.3064 1.3986 0.0059 +INF 0.0496
1.4929 NA -0.6350 +INF 0.1750

DAS# Asymptotic 0.0059 0.0455 -0.0690 +INF 0.4488
0.0049 NA -0.0687 +INF 0.4889
CONST Asymptotic -2.2431 5.0676 -INF 6.0923 0.3290

 

 

176

File: WBCFIRSTDAT
Model: RESPONSE=SEQUENCE+IMAGECA
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

 

 

#Obs: 12

#Groups: 12

Deviance : 9.3302 on 9 df

Tests ( 1 df) : <SEQUENCE>

Type of Test Statistic P-value P-mid

Likelihood Ratio 1.0467 0.3063 NA

Wald 1.0056 0.3160 NA

Scores 1.1129 0.2915 NA

Exact (Conditional Scores) DEGENERATE ? ?

Tests( 1 df) : <IMAGECA>

Type of Test Statistic P-value P- mid

Likelihood Ratio 3.7148 0.0539 NA

Wald 2.6134 0.1060 NA

Scores 3.3658 0.0666 NA

Exact (Conditional Scores) 2.8049 0.0778 0.0667

Tests ( 2 df) : <SEQUENCE,IMAGECA>

Type of Test Statistic P-value P- mid

Likelihood Ratio 6.9705 0.0306 NA

Wald 3.5975 0.1655 NA

Scores 5.7888 0.0553 NA

Exact (Conditional Scores) 5.3064 0 0505 0.0492

VARIABLE INFEREN CE < ------- PARAMETER ESTIMATION -------- > P-VALUE

SE(BETA) 95.0% (l-sided)BOUND ONE SIDED

SEQUENCE Asymptotic 1.6705 1.6658 -1.0695 +INF 0.1580
Exact Degenerate NA ?

IMAGECA Asymptotic -0.0474 0.0293 0.0008 0.0530

-0.0358 NA

CONST Asymptotic 7.0111 5.2408

-1.6092

-0.0011 0.0444

+INF

0.0905

 

 

177

Model: RESPONSE=SEQUENCE+DAS
Strat Var: <Unstratified>

Weight: <Unweighted>
#Obs: 12

#Groups: 4

Deviance :NA

Tests ( 1 df) : <SEQUENCE>

File: WBCFIRSTDAT

 

 

Type of Test Statistic P-value P-mid

Likelihood Ratio ? ? NA

Wald ? ? NA

Scores 4.0000 0.0455 NA

Exact (Conditional Scores) 3.3158 0.2143 0.1607

Tests ( 1 df) : <DAS>

Type of Test Statistic P-value P-mid

Likelihood Ratio ? ? NA

Wald ? ? NA

Scores 3.6923 0.0547 NA

Exact (Conditional Scores) 3.0769 0.1778 0.1111

Tests ( 2 df) : <SEQUENCE,DAS>

Type of Test Statistic P-value P~mid

Likelihood Ratio ? ? NA

Wald ? ? NA

Scores 5.8286 0.0542 NA

Exact (Conditional Scores) 5.3429 0.0657 0.0581

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE

TYPE BETA SE(BETA) 95.0% (l-sided)BOUND ONE SIDED
SEQUENCE Asymptotic ? ? . . ?
Exact 1.5099 <<MUE -0.4598 +INF 0.1071
DAS Asymptotic ? '2 w ? ? ?
Exact -1.3992 <<MUE -INF 0.6457 0.1333

CONST Asymptotic ? ? ? ? ?

Exact

 

 

178

Model: RESPONSE=SEQUENCE+DAS#
Strat Var: <Unstratified>
Weight: <Unweighted>

#Obs: 12

#Groups: 12

Deviance

: 11.0138 on 9df

Tests ( 1 df) : <SEQUENCE>

Type of Test Statistic
Likelihood Ratio 3.8428
Wald 2.7089
Scores 3.4932
Exact (Conditional Scores) 3.0000
Tests ( 1 df) : <DAS#>

Type of Test Statistic
Likelihood Ratio 2.0311
Wald 1.5780
Scores 1.8767
Exact (Conditional Scores) 1.5639

Tests ( 2 df) : <SEQUENCE,DAS#>

Type of Test Statistic
Likelihood Ratio 5.2868
Wald 2.9818
Scores 4.4798
Exact (Conditional Scores) 4.1065

File: WBCFIRSTDAT

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- >

TYPE BETA

SEQUENCE Asymptotic 2.8724 1.7452 0.0018

DASII

CONST

Exact 1.3476 <<MUE
Asymptotic -0.0735

Exact -0.0595

0.0585
NA

Asymptotic 5.9421
Exact

5.8725

-0.5396

+INF
+INF

0.0227
0.0199

+INF

P-mid
NA
NA
NA
0.1875

P—mid
NA
NA
NA
0.2278

P-mid
NA
NA
NA
0.1155

 

P-VALUE

SE(BETA) 95.0% (l-sided)BOUND ONE SIDED

0.0499
0.1250

0.1045
0.1222

0.1558

 

 

179

File: LYMPHSFIDAT
Model: RESPONSE=SEQUENCE+IMAGECA
Strat Var: <Unstratified>
Weight: <Unweighted>
#Obs: 12
#Groups: 12

Deviance 7.7133 on 9df

Tests ( 1 df) : <SEQUENCE>

 

 

 

 

Type of Test Statistic P-value P-mid

Likelihood Ratio 0.0110 0.9163 NA

Wald 0.0112 0.9158 NA

Scores 0.0112 0.9157 NA

Exact (Conditional Scores) DEGENERATE ? ?

Tests ( 1 df) : <IMAGECA>

Type of Test Statistic P-value P-mid

Likelihood Ratio 7.5631 0.0060 NA

Wald 3.7265 0.0536 NA

Scores 6.1631 0.0130 NA

Exact (Conditional Scores) 5.1359 0.017 8 0.0156

Tests ( 2 df) : <SEQUENCE,IMAGECA>

Type of Test Statistic P-value P-mid

Likelihood Ratio 8.9223 0.0115 NA

Wald 3.9291 0.1402 NA

Scores 6.8117 0.0332 NA

Exact (Conditional Scores) 6.2440 0 0216 0.0211

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED

SEQUENCE Asymptotic 0.2095 1.9824 -3.0512 +INF 0.4579

Exact Degenerate NA

IMAGECA Asymptotic c.0775 0.0401
Exact c.0555 NA

CONST Asymptotic 13.7646 7.5268
Exact

1.3841

?

-0.0115 0.0268
-0.0083 0.0178

+INF

0.0337

 

 

180

File: LYMPHSFIDAT

Model: RESPONSE=SEQUENCE+DAS
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12

#Groups: 4

Deviance : 0.7338 on 1 df

Tests ( 1 df) : <SEQUENCE>

Type of Test Statistic P-value P-mid

Likelihood Ratio 1.3592 0.2437 NA

Wald 1.2812 0.257 7 NA

Scores 1.3333 0.2482 NA

Exact (Conditional Scores) 1.1053 0.5810 0.4607

Tests ( 1 df) : <DAS>

Type of Test Statistic P-value P-mid

Likelihood Ratio 0.0000 1.0000 NA

Wald 0.0000 1.0000 NA

Scores 0.0000 1.0000 NA

Exact (Conditional Scores) 0.0000 1.0000 0.7 756

Tests ( 2 df) : <SEQUENCE,DAS>

Type of Test Statistic P-value P-mid

Likelihood Ratio 1.3592 0.5068 NA

Wald 1.2812 0.5270 NA

Scores 1.3333 0.5134 NA

Exact (Conditional Scores) 1.2222 0.8095 0.7 549

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE

TYPE BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED
SEQUENCE Asymptotic 1.3863 1.2247 -0.6282 +INF 0.1288
Exact 1.1247 NA -1.0956 +INF 0.2905
DAS Asymptotic 0.0000 1.2990 -2.1367 +INF 0.5000
Exact 0.0000 NA -2.6498 +INF 0.7 244

CONST Asymptotic -0.6931 1.2247 -INF 1.3214 0.2857

Exact

 

 

181

File: LYMPHSFI.DAT

Model: RESPONSE=SEQUENCE+DAS?
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12

#Groups: 12

Deviance : 14.2804 on 9 df

Tests ( 1 df) : <SEQUENCE>

Type of Test Statistic P-value P-mid

Likelihood Ratio 1.47 59 0.2244 NA

Wald 1.3527 0.2448 NA

Scores 1.4360 0.2308 NA

Exact (Conditional Scores) 2.0000 0.5000 0.3750

Tests ( 1 df) : <DASII>

Type of Test Statistic P-value P-mid

Likelihood Ratio 0.9959 0.3183 NA

Wald 0.9051 0.3414 NA

Scores 0.9651 0.3259 NA

Exact (Conditional Scores) 0.8043 0.3911 0.3867

Tests ( 2 df) : <SEQUENCE,DAS#>

Type of Test Statistic P-value P-mid

Likelihood Ratio 2.3551 0.3080 NA

Wald 1.8803 0.3906 NA

Scores 2.1912 0.3343 NA

Exact (Conditional Scores) 2.0086 0.3485 0.347 4

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE

TYPE BETA SE(BETA) 95.0% (l-sided)BOUND ONE SIDED
SEQUENCE Asymptotic 1.5190 1.3061 -0.6293 +INF 0.1224
Exact 0.8813 <<MUE -1.2456 +INF 0.2500
DAS# Asymptotic -0.0429 0.0451 -INF 0.0312 0.1707
Exact -0.0355 NA -INF 0.0293 0.1956

CONST Asymptotic 3.8650 4.8156 -4.0560 +INF 0.2111

Exact

 

 

182

File: CD3FIRST.DAT
Model: RESPONSE=SEQUENCE+IMAGECA
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12
#Groups: 12
Deviance : 9.7956 on 9 df
Tests ( 1 df) : <SEQUENCE>
Type of Test Statistic P-value P-mid
Likelihood Ratio 0.4743 0.4910 NA
Wald 0.4032 0.5255 NA
Scores 0.4267 0.5136 NA
Exact (Conditional Scores) DEGENERATE ? ?
Tests ( 1 df) : <IMAGECA>
Type of Test Statistic P-value P- mid
Likelihood Ratio 6.1603 0.0131 NA
Wald 2.8793 0.0897 NA
Scores 5.0599 0.0245 NA
Exact (Conditional Scores) 4.2166 0.0433 0.0417
Tests ( 2 df) : <SEQUENCE,IMAGECA>
Type of Test Statistic P-value P-mid
Likelihood Ratio 6.5050 0.0387 NA
Wald 3.2340 0.1985 NA
Scores 5.3598 0.0686 NA
Exact (Conditional Scores) 4.9132 0.0682 0.067 6
VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED
SEQUENCE Asymptotic -1.3268 2.0896 -INF 2.1103 0.2627
Exact Degenerate NA ? ? ?
IMAGECA Asymptotic -0.0667 0.0393 -INF -0.0020 0.0449
Exact -0.0536 NA -INF -0.0063 0.0233
CONST Asymptotic 11.7758 7.3620 -0.3336 +INF 0.0549

Exact

 

 

183

Model: RESPONSE=SEQUENCE+DAS
Strat Var: <Unstratified>

Weight: <Unweighted>

#Obs: 12

IlGroups: 4

Deviance 0.1913 on 1 df

Tests ( 1 df) : <SEQUENCE>

File: CD3FIRST.DAT

 

 

Type of Test Statistic P-value P-mid

Likelihood Ratio 0.3498 0.5542 NA

Wald 0.3438 0.5576 NA

Scores 0.3478 0.5553 NA

Exact (Conditional Scores) 0.2877 1.0000 0.8155

Tests ( 1 df) : <DAS>

Type of Test Statistic P-value P-mid

Likelihood Ratio 0.1756 0.6752 NA

Wald 0.1751 0.6757 NA

Scores 0.1765 0.6744 NA

Exact (Conditional Scores) 0.1471 1.0000 0.7 933

Tests ( 2 df) : <SEQUENCE,DAS>

Type of Test Statistic P-value P-mid

Likelihood Ratio 0.5203 0.7 709 NA

Wald 0.4988 0.7793 NA

Scores 0.5143 0.7733 NA

Exact (Conditional Scores) 0.4714 1.0000 0.9217

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE

TYPE BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED
SEQUENCE Asymptotic 0.7037 1.2002 1.2704 +INF 0.2788
Exact 0.5786 NA -1.6932 +INF 0.5000
DAS Asymptotic -0.5264 1.2582 -INF 1.5431 0.3378
Exact -0.4366 NA -INF 2.1018 0.5733

CONST Asymptotic -0.3519 1.1796 -INF 1.5884 0.3827

Exact

 

 

184

File: CD3FIRST.DAT
Model: RESPONSE=SEQUENCE+DAS#
Strat Var: <Unstratified>
Weight: <Unweighted>
#Obs: 12
llGroups: 12
Deviance : 15.2374 on 9 df

Tests ( 1 df) : <SEQUENCE>

 

 

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.3662 0.5451 NA
Wald 0.3586 0.5493 NA
Scores 0.3637 0.5465 NA
Exact (Conditional Scores) 0.3333 1.0000 0.8125
Tests ( 1 df) : <DAS#>

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.7185 0.3966 NA
Wald 0.677 2 0.4106 NA
Scores 0.7065 0.4006 NA
Exact (Conditional Scores) 0.5888 0.4933 0.4883
Tests ( 2 df') : <SEQUENCE,DAS#>

Type of Test Statistic P-value P- mid
Likelihood Ratio 1.0632 0.587 7 NA
Wald 0.9578 0.6195 NA
Scores 1.0292 0.5977 NA
Exact (Conditional Scores) 0.9434 0.6162 0.6143
VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE

TYPE BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED
SEQUENCE Asymptotic 0.7375 1.2315 -1.2882 +INF 0.2746
Exact 0.6932 NA -1.8549 +INF 0.5000
DASII Asymptotic -0.0346 0.0421 -INF 0.0346 0.2053
Exact -0.0287 NA -INF 0.0343 0.2433
CONST Asymptotic 2.9962 4.5238 -4.4448 +INF 0.2539

Exact

 

 

185

File: CD4FIRST.DAT
Model: RESPONSE=SEQUENCE+IMAGECA
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12

#Groups: 12

Deviance : 7.7133 on 9 df

Tests ( 1 df) : <SEQUENCE>

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.0110 0.9163 NA
Wald 0.0112 0.9158 NA
Scores 0.0112 0.9157 NA
Exact (Conditional Scores) DEGENERATE ? ?
Tests ( 1 df) : <IMAGECA>

Type of Test Statistic P-value P-mid
Likelihood Ratio 7 .5631 0.0060 NA
Wald 3.7265 0.0536 NA
Scores 6.1631 0.0130 NA
Exact (Conditional Scores) 5.1359 0.0178 0.0156
Tests ( 2 df) : <SEQUENCE,IMAGECA>

Type of Test Statistic P-value P-mid
Likelihood Ratio 8.9223 0.0115 NA
Wald 3.9291 0.1402 NA
Scores 6.8117 0.0332 NA
Exact (Conditional Scores) 6.2440 0.0216 0.0211
VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE

SE(BETA) 95.0% (l-sided)BOUND ONE SIDED

SEQUENCE Asymptotic 0.2095 1.9824

Exact Degenerate NA

IMAGECA Asymptotic -0.0775 0.0401

CONST

—0.0555 NA

Asymptotic 13.7 646 7 .5268

-3.0512

1.3841

+INF

0.4579
?

-0.0115 0.0268
-0.0083 0.017 8

+INF

0.0337

 

 

186

File: CD4FIRST.DAT
Model: RESPONSE=SEQUENCE+DAS
Strat Var: <Unstratified>
Weight: <Unweighted>

#Obs: 12

#Groups: 4

Deviance : 0.7 338 on 1 df

Tests ( 1 df) : <SEQUENCE>

Type of Test Statistic P-value P-mid
Likelihood Ratio 1.3592 0.2437 NA
Wald 1.2812 0.257 7 NA
Scores 1.3333 0.2482 NA
Exact (Conditional Scores) 1.1053 0.5810 0.4607
Tests ( 1 df) : <DAS>

Type of Test Statistic P-value P- mid
Likelihood Ratio 0.0000 1.0000 NA
Wald 0.0000 1.0000 NA
Scores 0.0000 1.0000 NA
Exact (Conditional Scores) 0.0000 1.0000 0.7 756
Tests ( 2 df) : <SEQUENCE,DAS>

Type of Test Statistic P-value P-mid
Likelihood Ratio 1.3592 0.5068 NA
Wald 1.2812 0.5270 NA
Scores 1.3333 0.5134 NA
Exact (Conditional Scores) 1.2222 0.8095 0.7 549

J
-

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0% (1csided)BOUND ONE SIDED

SEQUENCE Asymptotic 1.3863 1.2247 -0.6282 +INF 0.1288

Exact 1.1247 NA -1.0956 +INF 0.2905
DAS Asymptotic 0.0000 1.2990 -2.1367 +INF 0.5000
Exact 0.0000 NA -2.6498 +INF 0.7 244
CONST Asymptotic -0.6931 1.2247 -INF 1.3214 0.2857

Exact

 

 

187

File: CD4FIRST.DAT
Model: RESPONSE=SEQUENCE+DAS/I
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12

#Groups: 12

Deviance : 14.2804 on 9 df

Tests ( 1 df) : <SEQUENCE>

Type of Test Statistic P-value P-mid

Likelihood Ratio 1.4759 0.2244 NA

Wald 1.3527 0.2448 NA

Scores 1.4360 0.2308 NA

Exact (Conditional Scores) 2.0000 0.5000 0.3750

Tests ( 1 df) : <DAS#>

Type of Test Statistic P-value P-mid

Likelihood Ratio 0.9959 0.3183 NA

Wald 0.9051 0.3414 NA

Scores 0.9651 0.3259 NA

Exact (Conditional Scores) 0.8043 0.3911 0.3867

Tests ( 2 df) : <SEQUENCE,DAS#>

Type of Test Statistic P-value P-mid

Likelihood Ratio 2.3551 0.3080 NA

Wald 1.8803 0.3906 NA

Scores 2.1912 0.3343 NA

Exact (Conditional Scores) 2.0086 0.3485 0.347 4

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE

SE(BETA) 95.0% (1-sided)BOUND ONE SIDED
SEQUENCE Asymptotic 1.5190 1.3061 -0.6293 +INF 0.1224
0.8813 <<MUE -1.2456 +1NF 0.2500
DAS# Asymptotic -0.0429 0.0451 0.0312 0.1707
-0.0355 NA 0.0293 0.1956

CONST Asymptotic 3.8650 4.8156 -4.0560 +INF 0.2111

 

 

188

File: CD8FIRST.DAT
Model: RESPONSE=SEQUENCE+IMAGECA
Strat Var: <Unstratified>
Weight: <Unweighted>

 

 

#Obs: 12

#Groups: 12

Deviance : 6.8665 on 9 df

Tests ( 1 df) : <SEQUENCE>

Type of Test Statistic P-value P-mid

Likelihood Ratio 3.3818 0.0659 NA

Wald 0.5568 0.4556 NA

Scores 2.0183 0.1554 NA

Exact (Conditional Scores) DEGENERATE ? ?

Tests ( 1 df) : <IMAGECA>

Type of Test Statistic P-value P-mid

Likelihood Ratio 9.7691 0.0018 NA

Wald 0.6837 0.4083 NA

Scores 6.5726 0.0104 NA

Exact (Conditional Scores) 5.4772 0.0200 0.0188

Tests ( 2 df) : <SEQUENCE,IMAGECA>

Type of Test Statistic P-value P-mid

Likelihood Ratio 9.7 691 0.0076 NA

Wald 0.9017 0.6371 NA

Scores 6.57 26 0.0374 NA

Exact (Conditional Scores) 6.0249 0.0390 0.0384

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE

TYPE BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED

SEQUENCE Asymptotic -9.0868 12.1780 -INF 10.9442 0.227 8
Exact Degenerate NA ? ?

IMAGECA Asymptotic -0.2062 0.2493 -INF 0.2040 0.2042
Exact -0.1661 NA -INF -0.0136 0.0100

CONST Asymptotic 40.4315 49.6634 -41.257 6 +INF

Exact

0.2078

 

 

1’;

189

Model: RESPONSE=SEQUENCE+DAS
Strat Var: <Unstratified>

Weight: <Unweighted>

#Obs: 12

#Groups: 4

Deviance 0.0000 on 1 df

File: CD8FIRST.DAT

Tests ( 1 df) : <SEQUENCE>

 

 

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.0000 1.0000 NA
Wald 0.0000 1.0000 NA
Scores 0.0000 1.0000 NA
Exact (Conditional Scores) 0.0000 1.0000 0.7905
Tests ( 1 df) : <DAS>

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.0000 1.0000 NA
Wald 0.0000 1.0000 NA
Scores 0.0000 1.0000 NA
Exact (Conditional Scores) 0.0000 1.0000 0.7 800
Tests ( 2 df) : <SEQUENCE,DAS>

Type of Test Statistic P—value P- mid
Likelihood Ratio 0.0000 1.0000 NA
Wald 0.0000 1.0000 NA
Scores 0.0000 1.0000 NA
Exact (Conditional Scores) 0.0000 1.0000 0.9048
VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE

TYPE

SEQUENCE Asymptotic 0.0000 1.1547

Exact 0.0000 NA
DAS Asymptotic 0.0000 1.2247
Exact 0.0000 NA
CONST Asymptotic 0.0000 1.1547

Exact

-INF
-INF

-INF

1.8993
2.2410

2.0145
2.4932

1.8993

BETA SE(BETA) 95.0% (1-sided)BOUND ONE SIDED

0.5000
0.7095

0.5000
0.7200

0.5000

190

File: CD8FIRST.DAT
Model: RESPONSE=SEQUENCE+DAS#
Strat Var: <Unstratified>
Weight: <Unweighted>

#Obs: 12

#Groups: 12

Deviance : 16.3412 on 9 df

Tests ( 1 df) : <SEQUENCE>

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.0000 1.0000 NA
Wald 0.0000 1.0000 NA
Scores 0.0000 1.0000 NA
Exact (Conditional Scores) 0.0000 1.0000 0.7 500
Tests ( 1 df) : <DAS#>

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.2943 0.587 5 NA
Wald 0.2872 0.5920 NA
Scores 0.2919 0.5890 NA
Exact (Conditional Scores) 0.2433 0.6200 0.6175
Tests ( 2 df) : <SEQUENCE,DAS#>

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.2943 0.8632 NA
Wald 0.2872 0.8662 NA
Scores 0.2919 0.8642 NA
Exact (Conditional Scores) 0.2676 0.8355 0.8344

 

 

VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0%(1-sided)BOUND ONE SIDED

SEQUENCE Asymptotic 0.0000 1.1690 -1.9228 +INF 0.5000

Exact 0.0000 NA -INF 3.6508 0.7500

DASII Asymptotic -0.0212 0.0395 -INF 0.0439 0.2960
Exact -0.017 7 NA -INF 0.0404 0.3100
CONST Asymptotic 2.2859 4.3464 -4.8633 +INF 0.2995

Exact

 

 

191

File: SEGSFIRSDAT
Model: RESPONSE=SEQUENCE+IMAGECA
Strat Var: <Unstratified>
Weight: <Unweighted>

#Obs: 12

#Groups: 12

Deviance : 12.4432 on 9 df

Tests ( 1 df) : <SEQUENCE>

Type of Test Statistic P-value P-mid
Likelihood Ratio 1.8117 0.1783 NA
Wald 1.6652 0.1969 NA
Scores 1.8573 0.1729 NA
Exact (Conditional Scores) 0.5000 1.0000 0.6667
Tests ( 1 df) : <IMAGECA>

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.6017 0.4379 NA
Wald 0.5722 0.4494 NA
Scores 0.5975 0.4395 NA
Exact (Conditional Scores) 0.4980 0.5222 0.5111
Tests ( 2 df) : <SEQUENCE,IMAGECA>

Type of Test Statistic P-value P-mid
Likelihood Ratio 3.8574 0.1453 NA
Wald 2.9294 0.2311 NA
Scores 3.5656 0.1682 NA
Exact (Conditional Scores) 3.2685 0.2083 0.2071

 

 

 

 

VARIABLE INFEREN CE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE BETA SE(BETA) 95.0%(1-sided)BOUND ONE SIDED

SEQUENCE Asymptotic 1.9077 1.4783 -0.5240 +INF 0.0985

Exact -0.6929 <<MUE -3.6378 +INF 0.6667
IMAGECA Asymptotic -0.0163 0.0215 -1NF 0.0191 0.2247
Exact -0.0134 NA -INF 0.0175 0.2333

CONST Asymptotic 1.4513 4.1241 -5.3323 +INF 0.3625
Exact

 

 

192

Model: RESPONSE=SEQUENCE+DAS

Strat Var: <Unstratified>
Weight: <Unweighted>
#Obs: 12

File: SEGSFIRS.DAT

 

 

#Groups: 4
Deviance 2.7692 on 1 df
Tests ( 1 df) : <SEQUENCE>
Type of Test Statistic P-value P-mid
Likelihood Ratio 3.3171 0.0686 NA
Wald 2.7212 0.0990 NA
Scores 3.1304 0.0768 NA
Exact (Conditional Scores) 2.5890 0.2619 0.2024
Tests ( 1 df) : <DAS>
Type of Test Statistic P-value P- mid
Likelihood Ratio 0.2318 0.6302 NA
Wald 0.2267 0.6340 NA
Scores 0.2308 0.6310 NA
Exact (Conditional Scores) 0.1923 1.0000 0.7778
Tests ( 2 df) : <SEQUENCE,DAS>
Type of Test Statistic P-value P-mid
Likelihood Ratio 3.4876 0.1749 NA
Wald 2.7619 0.2513 ' NA
Scores 3.2571 0. 1962 NA
Exact (Conditional Scores) 2.9857 0.3131 0.287 9
VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE
TYPE SE(BETA) 95.0% (l-sided)BOUND ONE SIDED
SEQUENCE Asymptotic 2.3589 1.4300 0.0068 +INF 0.0495
Exact 1.8615 NA -0.5332 +INF 0.1310
DAS Asymptotic -0.7002 1.47 08 0.317 0
Exact -0.5824 NA 2.3390 0.57 78
CONST Asymptotic -1.1795 1.3789 0.1962

Exact

 

 

193

Model: RESPONSE=SEQUENCE+DAS/l
Strat Var: <Unstratified>
Weight: <Unweighted>

#Obs: 12

#Groups: 12

Deviance

: 13.0283 on 9df

Tests ( 1 df) : <SEQUENCE>

File: SEGSFIRS.DAT

 

 

Type of Test Statistic P-value P- mid
Likelihood Ratio 3.2600 0.0710 NA
Wald 2.7195 0.0991 NA
Scores 3.0889 0.07 88 NA
Exact (Conditional Scores) 1.9565 0.3500 0.2750
Tests ( 1 df) : <DAS#>

Type of Test Statistic P-value P-mid
Likelihood Ratio 0.0166 0.897 6 NA
Wald 0.0165 0.897 7 NA
Scores 0.0166 0.8976 NA
Exact (Conditional Scores) 0.0138 0.9444 0.9111
Tests ( 2 df) : <SEQUENCE,DAS#>

Type of Test Statistic P—value P- mid
Likelihood Ratio 3.2723 0.1947 NA
Wald 2.7 229 0.2563 NA
Scores 3.0980 0.2125 NA
Exact (Conditional Scores) 2.8398 0.3359 0.3321
VARIABLE INFERENCE < ------- PARAMETER ESTIMATION -------- > P-VALUE

TYPE BETA

SEQUENCE Asymptotic 2.3064 1.3986 0.0059

DASII

CONST

Exact 1.4929 NA -0.6350
Asymptotic 0.0059 0.0455 -0.0690
Exact 0.0049 NA -0.0687
Asymptotic -2.2431 5.067 6 -INF

Exact

I
L

+INF
+INF

+INF
+INF

6.0923

SE(BETA) 95.0% (1-sided)BOUND ONE SIDED

0.0496
0.1750

0.4488
0.4889

0.3290

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