Improving Informal Caregivers' and Cancer Survivors' Psychological Distress, Symptom Management and Health Care Use
Phase: NA
Conditions: Cancer
Sponsor: University of Arizona

 
 
 
 
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SCIENTIFIC REVIEW COMMITTEE 
 
Title: Improving Informal Caregivers' and Cancer Survivors' Psychological Distress, 
Symptom Management and Health Care Use 
 
KEY PERSONNEL  
 
Name 
Organization 
Role on Project 
Terry Badger, PhD 
University of Arizona 
Principal Investigator  
 
Alla Sikorskii, PhD 
Michigan State University 
Co-Investigator 
 
 
Chris Segrin, PhD 
University of Arizona 
Co-Investigator 
 
 
Tracy Crane, PhD 
University of Arizona 
Co-Investigator 
Pavani Chalasani, MD, MPH 
University of Arizona 
Co-Investigator 
 
  
Charles Given, PhD 
Michigan State University 
Consultant 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
INDEX  
 
 
 
 
 
 
Section 
Schema/Study Flowchart 
Background & Rationale 
1.0 
Objectives/Specific Aims 
2.0 
Sample Eligibility Criteria 
3.0 
Participant Registration 
4.0 
Research Design, Methods and Procedures 
5.0 
Measures/Data Collection Instruments 
6.0 
Detailed Description of Intervention 
7.0 
Statistical Considerations 
8.0 
Study Timeline 
9.0 
Data Safety and Monitoring Plan 
10.0 
Reporting Adverse Events 
11.0 
Patient Consent and Peer Judgment 
12.0 
References 
13.0 
Appendices: 
14.0 
 
 
 

 
 
 
 
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SCHEMA OR FLOWCHART 
Insert schematic or diagram outlining study design and procedures 
 
 
 

 
 
 
 
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1.0 
BACKGROUND & RATIONALE 
 
Scientific background and basis for hypothesis(es) to be tested. Include justification for 
conducting study and results of similar studies or pilot data.   
 
Informal Caregivers of Cancer Survivors. Informal caregivers are people within the survivor’s social 
network, related by blood or emotional attachment, who provide emotional, informational and/or instrumental 
support.52 The value of this unpaid labor force of 44 million caregivers in the United States53 is estimated to be 
at least $306 billion annually,54,55 with 5.7 million caregivers providing care to cancer survivors.56 Caregivers of 
cancer patients become involved in complex care activities57 for an average of 14 months.58,59 A shift to patient-
centered care is facilitated by engaging the caregivers and requires the understanding of survivors’ and 
caregivers’ outcomes resulting from such engagement.60,61 Caregivers experience a range of psychological 
problems, disruption of daily routines, financial burdens, and role changes that accompany the care recipient’s 
cancer diagnosis and treatment.62-64 Caring for the survivor often has negative consequences to the caregiver’s 
family resources and health.65,66 Caregivers have poorer physical health than those in the general population, 
reflected by higher prevalence of arthritis, chronic back pain, and heart disease.67 Between 30-50% of 
caregivers experience increased psychiatric morbidity, fatigue, sleep impairment. 68-70 Psychological distress 
among caregivers is often present at levels equal to or greater than among cancer survivors.40,71-81 Risk of 
these morbidities is particularly high among caregivers who are female,82-84 less educated,85 younger in age,86 
and who are caring for younger adult survivors.87,88 These findings explain increased use of health care 
services among caregivers of those recently diagnosed with cancer.89,90  
Meaningful involvement in the survivor’s care may also have a positive impact on caregivers’ health,91-94 
which improves the quality of care they provide to the survivors.95-98 For these reasons, interventions providing 
caregivers with tools such as the SMSH are highly valuable.99,100 Further, psychosocial interventions that 
directly improve caregivers’ health,101 reduce psychological distress and address problem-solving and 
communication skills,102 are also potentially beneficial to survivors and their caregivers. The proposed research 
will deliver psychosocial (TIP-C) and educational (SMSH) interventions and test their optimal sequencing for 
survivors with known levels of distress and symptom severity as well as their impact on their caregivers who 
often experience distress and associated other symptoms (e.g., poor sleep, fatigue). 
Symptom burden in cancer survivors. Physical and psychological symptoms are aggravated while cancer 
survivors are in treatment.103,104 Survivors’ symptom burden105-112 is often the primary reason for altering or 
stopping chemotherapy113,114 leading to suboptimal treatment at the very least,115-117 and life threatening 
recurrence or metastasis at the extreme.118,119 The prevalence of specific symptoms (e.g., fatigue, pain, 
depression, anxiety, disturbed sleep, nausea and vomiting, neuropathy)120-125 varies by cancer diagnosis, 
treatment type, gender, ethnicity and age,98,99 variables that will be empirically tested as potential tailoring 
factors in decision rules that will be formulated in this research. The number of symptoms reported by cancer 
survivors can be as high as 14.126 127 Approximately 30% of cancer survivors report psychological distress 
(depression and anxiety). Major depression occurs in approximately 16%, and subthreshold depressive 
disorders occur in 22% of cancer survivors during treatment.28 These prevalence rates are about 3 times higher 
than in the general population. Even when depression and anxiety do not meet threshold for clinical diagnoses, 
these symptoms are still associated with significant health impairments, yet are highly treatable,128,129 and will 
be in this trial, potentially saving healthcare costs and improving outcomes. Further, in our past studies, Latinas 
with breast cancer reported a higher number and more symptom burden/distress than did non-Hispanic white 
women with similar diagnoses.31 Therefore, we will enroll an ethnically diverse sample (includes 30% 
Hispanic/Latino participants) in this trial to address a significant symptom management need. Given the 
increasing population of Hispanic cancer survivors, providing and testing an intervention in the participant’s 
primary language could have national significance. A series of longitudinal studies130-133 including those 
conducted by this team130,131, found an association between increasing symptom prevalence and poorer 
physical and emotional functioning. Our team’s past work134 and that of others135 documented the associations 
between reductions in symptoms and lower number of hospitalizations, office and ED visits.10 The proposed 
interventions are significant because they will address the multiple symptoms experienced by survivors during 
treatment and the associated health care service use. 
 

 
 
 
 
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2.0 
OBJECTIVE(S)/SPECIFIC AIMS 
 
Purpose and specific aims of the study. 
 
Informal caregivers, typically family members or friends, provide more than half of the care needed for the 5.7 
million cancer survivors in the United States, often with negative health consequences. At least 30% of 
survivors and their caregivers report psychological distress (depression and anxiety) and such distress may 
interfere with optimal symptom management. This study will support both members of the survivor-caregiver 
dyad in the management of the survivor’s cancer- and treatment-related symptoms and the dyad’s 
psychological distress. 
Design: We will use the sequential multiple assignment randomized trial (SMART) 
design, a newer adaptive design. The SMART moves beyond a traditional RCT to a precision approach to 
determine the right treatment at the right dose with the right sequence for the right survivor-caregiver dyad. We 
will use two evidence-based interventions: Symptom Management and Survivorship Handbook (SMSH) and 
Telephone Interpersonal 
Counseling (TIP-C). While we have established the overall efficacy of these interventions, but individuals differ 
in responses. When an intervention does not initially work, clinic logic is to either extend the timeframe or 
prescribe a different intervention. Yet, such alternatives are seldom tested nor evidence-based. However, they 
will be in this study. 
Sample: We will enroll 298 survivors with elevated depression or anxiety who are undergoing chemotherapy or 
targeted therapy for a solid tumor and their 298 caregivers. 
Procedure: Dyads will be initially randomized to SMSH alone or TIP-C +SMSH. If the survivor’s elevated 
depression or anxiety does not respond to SMSH alone by week 4, dyads will be re-randomized to continue 
with SMSH to give it more time or to TIP-C+SMSH. Outcome data will be collected at baseline, weeks 13 
(post-intervention) and 17 (follow-up). Assessments during weeks 1-12 will document changes in symptoms, 
intervention receipt, enactment and fidelity. 
Specific aims: 1) Determine if dyads in the TIP-C+SMSH as compared to the SMSH alone group created by 
the first randomization will have: a) lower depression, anxiety, and summed severity of 13 other symptoms at 
weeks 1-12, 13, and 17 (primary outcomes); b) lower use of healthcare services (hospitalizations, urgent 
care or emergency department [ED] visits) during17 weeks (secondary outcomes); c) greater self-efficacy, 
social support, and lower caregiver burden during weeks 13 and 17 (potential mediators). 2) Among 
nonresponders to the SMSH alone after 4 weeks, determine if dyads assigned to TIP-C+SMSH as compared 
to the SMSH alone group created by the second randomization will have better primary and secondary 
outcomes and potential mediators at weeks 5-12, 13, and 17. 3) Test the interdependence in survivor’s and 
caregiver’s 
primary and secondary outcomes. 4) Determine which characteristics of the dyad are associated with 
responses to the SMSH alone during weeks 1-4 and optimal outcomes for the dyad during weeks 1-12, 13 and 
17 so as to determine tailoring variables for the decision rules of individualized sequencing of interventions. 
Findings will be used to improve symptom management and reduce distress in survivor-caregiver dyads.  
 
 
3.0 
SAMPLE ELIGIBILITY CRITERIA 
 
Specific inclusion/exclusion requirements which must be met for entry.   
 
Inclusion criteria for the survivors are: 1) age 18 or older; 2) undergoing chemotherapy, hormonal therapy, or 
targeted therapy for a solid tumor cancer; 3) able to perform basic activities of daily living; 4) cognitively 
oriented to time, place, and person (determined by recruiter); 5) reporting severity of >2 on depression or >4 on 
anxiety using a 0-10 standardized scale; 6) able to speak and understand English or Spanish; 7) access to a 
telephone and 8) has a caregiver who can be in any relationship role (e.g., spouse, sibling, parent, friend) who 
can participate with them. 
 
Exclusion criteria are: 1) diagnosis of a psychotic disorder in the health record; 2) nursing home resident; 3) 
bedridden; 4) currently receiving counseling and/or psychotherapy. Inclusion criteria for the caregivers are: 1) 

 
 
 
 
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age 18 or older; 2) able to speak and understand English or Spanish; 3) access to a telephone; 4) not currently 
receiving counseling and/or psychotherapy; and 5) not currently treated for cancer. 
 
The advantages of these inclusion criteria are in study generalizability. Solid tumors have been selected 
because cancer- and treatment-related symptoms can be effectively managed in this population with the two 
interventions (see section C2). Site of cancer and other prognostic factors will be controlled in randomization 
(see C7). Thus, groups compared in study hypotheses will be balanced on disease and treatment 
characteristics. The cut-offs of >2 on depression and >4 on anxiety indicate their presence at moderate or 
severe levels based on established interference-based cut-points.224 The need to screen on depression and 
anxiety is premised on a meta-analysis that psychosocial interventions are most beneficial for those with 
elevated distress.225,226 By allowing survivors to select their own caregivers, findings would be generalizable to 
the cancer survivor population who may be single or rely on other people for support. The exclusion of 
caregivers currently treated for cancer will preserve the distinguishability of the “survivor” and “caregiver” roles 
within the dyad. Our prior research indicates that participation in counseling and/or psychotherapy for either 
dyad member is rare during survivors’ treatment. These exclusion criteria will not substantially limit the 
population but will eliminate potential confounding of the intervention effects with extraneous influences. 
 
4.0 
PARTICIPANT RECRUITMENT/ENROLLMENT 
 
Describe how study participants are to be recruited and enrolled in the study with appropriate 
contact phone numbers, etc. Stratification factors, participant characteristics which are 
balanced across treatment arms or used to determine intervention doses are described here.  
The randomization scheme is included here if applicable. 
 
Sample. We will recruit 298 dyads (see below for power), allowing for 22% attrition, for a final post-attrition 
sample of 232 dyads available for analysis. Based on the demographic characteristics of the AZ population, 
the sample will consist of approximately 60% non-Hispanic white, 30% Hispanic/Latino, 3% each African 
American and Asian American, and 4% American Indian participants. There is an ample pool of cancer 
survivors available to meet enrollment targets. Of the estimated 35,810 newly diagnosed cases of cancer227 in 
AZ in 2017, the majority were solid tumors. Conservatively, we will have easy access to about 3000 survivors 
with our existing sites, of which approximately one third (n=1000) should satisfy the inclusion criteria. Following 
the initial recruitment contact, we anticipate about 10% will consent to participate, thus we can recruit 100 
dyads per year. Given this team’s past successful recruitment at the UACC and community sites (letters of 
support), the proposed study will easily meet the recruitment goal of 99 dyads each year of the 3 years of 
recruitment. 
Sample size and power considerations. To determine sample size, we started at the right of the 
schematic in Figure 1 (the second randomization) and moved from right to left to determine the needed 
number of consenting dyads. To power the comparisons on primary outcomes for the groups created by the 
second randomization (Aim 2), we used the effect size of Cohen’s d=0.39 (adjusted for baseline), the smallest 
seen in the preliminary data for TIP-C against an educational intervention (Table 1) to conservatively estimate 
sample size requirements. We further adjusted this effect size for the reduction in error variance due to 10 
repeated measures of primary outcomes. In past studies, Pearson correlation coefficients between pairs of 
repeated measures of depression, anxiety and summed severity of other symptoms ranged from r=0.36 to 
0.77, resulting in the range of the adjusted effect sizes from d=0.54 to 0.84. Using the smallest adjusted 
d=0.54, the required sample size is 60 per group created by the second randomization, for power of .80 or 
greater in two-tailed tests at the 0.05 level of significance. 
Moving from left to right in Figure 1 (schematic), 120 dyads from two groups created by the second 
randomization will be non-responders to the SMSH alone. From past work, response rate to the SMSH on 
depression and anxiety was approximately 30%,228 therefore 120 non-responders will be 70% of 172 
randomized to the SMSH alone in the first randomization. The comparison of these 172 to 60 dyads allocated 
to the TIP-C+SMSH in the first randomization will have power of 0.95 to detect the effect size of 0.54 (adjusted 
for the repeated measures) in testing the hypothesis associated with Aim 1. The tests of mediation and 
interdependence effects in Aim 3 will have an even greater power because of further reduction in error 
variance. Aim 4 is exploratory, thus formal power considerations are not applicable. In summary, the total 

 
 
 
 
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required post-attrition sample size for all specific aims is N=232. To account for 22% attrition based on past 
work, we will need 298 dyads to consent. 
Recruitment and retention of participants.  
Accrual. Recruiters have research roles and do not provide direct care at the sites. They will approach 
survivors during clinic visits and explain the study. Survivors can choose to consent at that time or take the 
packet home to discuss with their caregivers. Recruiters will follow up during a clinic visit or by phone to further 
explain the study, answer questions, and discuss the study with caregivers. If the survivor or caregiver give 
verbal consent over the phone, the participant will return the signed consent forms with witnessed signature in 
a postage paid envelope. If the consent forms are not returned within one week, the recruiter will call the 
participant to ask that the signed consent forms be mailed if they wish to participate. 
Recruiter training. The study Coordinator will conduct recruiter training that includes didactic information, 
role-playing, and return demonstration of recruiting per script. Recruiters will introduce the study to survivors: 
17-week study duration, randomizations to TIP-C+SMSH versus SMSH alone to help manage symptoms, 12 
weekly calls and three interviews, no cost to study participation, risks/benefits, and incentives. 
Subject incentives. We will provide gift cards for completing baseline, 13 and 17-week assessments. 
Incentive payments not only significantly improve recruitment rates229, but there are no significant differences in 
key dependent variables for those offered versus those not offered an incentive.230 Provision of incentives 
equivalent to the demands of participation is vital to successfully recruiting minorities into research and getting 
a culturally representative diverse sample.231-234 After every assessment, participants will receive thank you 
letters and gift cards from a large retail merchant in graduated amounts ($40 after 1st, $50 after 2nd and $60 
after 3rd). The total compensation will be $150 for about 6-10 hours of participants’ time over 17 weeks. 
Strategies to minimize attrition. 1) Recruiters will emphasize the importance of participating in the entire 
intervention each week. 2) Survivors and caregivers will be asked to mark their calendars for study calls. 3) 
email or text reminders about upcoming telephone contacts will be sent if agreed to by participants. 4) Weekly 
calls will maintain contact with all participants for the entire study duration. 5) Graduated compensation for 
assessments will be provided. These strategies have worked well in the past. Participants will be assured of 
the confidentiality of all information and that refusing to participate will not alter their care. Survivors will 
continue to receive standard medical and nursing care and may seek care from their health providers for any 
health problems that arise. For dyads that refuse to participate, the recruiter will seek consent to collect their 
de-identified demographic data and ask about the reason for refusal. These data help us understand who 
declines and contribute to external validity and generalizability of the findings. 
Community ties and cultural sensitivity. We use experienced staff members with extensive ties to the 
local survivorship communities. The study brochures will be developed in English and in Spanish with 
community advisors.31,210 Seven principles of language competence, cultural competence, ethical conduct, 
mission or purpose, empathy, graciousness and credibility235 will be incorporated in all interactions. We will 
show cultural sensitivity along two dimensions.236,237 Surface structure involves matching messages to 
observable ‘superficial’ characteristics of the target population (e.g., speaking English or Spanish). Deep 
structures involve incorporating some of the socio-cultural, historical, environmental and psychological forces 
that influence health behaviors. For example, we will incorporate the value of personalismo by talking about 
participants’ lives at the beginning of sessions. Participants from past studies have appreciated the flexibility 
and respect (respeto) inherent in our caregiver definition, which allowed survivors to choose the person to 
participate. 238 These techniques allow us to personalize our interactions, addressing both personal and cancer 
issues of concern. This approach is critical to gain trust (confianza). 
Randomization. Following the baseline interview, dyads will be randomized to either SMSH alone (N=172 
post-attrition) or to TIP-C+SMSH (N=60 post-attrition). A minimization algorithm will be programmed by Dr. 
Sikorskii to balance arms by recruitment location, site of cancer, stage of cancer (early versus late), type of 
treatment, and survivor’s relationship to the caregiver (spouse vs non-spouse).239 The second randomization 
will occur for those who do not respond to the SMSH alone after the first 4 weeks using the same approach as 
the first randomization except in 1:1 ratio. The study coordinator will run the computer algorithm from the main 
study office (Tucson) to ensure allocation, concealment, and blinding. 
 
5.0 
RESEARCH DESIGN, METHODS AND PROCEDURES  
 
Describe research design, methods and all study procedures   

 
 
 
 
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We will use two evidence-based interventions extensively tested against active and passive controls in 
traditional randomized controlled trials (RCTs). While overall efficacy of these interventions has been 
established7,15,16,29-31, individuals differ in their responses. When an intervention does not initially work, clinical 
logic is to either extend the timeframe or prescribe a different intervention. Yet, these alternatives are seldom 
tested and not evidence-based. The proposed project advances beyond a traditional RCT of testing fixed “one 
size fits all” interventions to the sequential multiple assignment randomized trial (SMART) 32-37 design to build 
the evidence base for intervention sequencing that accounts for heterogeneity of responses. The first 
intervention, a printed Symptom Management and Survivorship Handbook (SMSH) with strategies for self-
management of symptoms common during chemotherapy15,16,30 will be given to both survivor and caregiver 
(the dyad). SMSH strategies, if successfully enacted by the dyad, produce positive symptom responses for the 
survivor. However, psychological distress of the survivor or the caregiver may diminish the receipt and 
enactment of the SMSH strategies and also exacerbate the severity of other symptoms38 which, in turn, 
produces poor symptom responses.39 Research by this team40,41 and others42-48 has documented dyadic effects 
where survivors’ psychosocial distress impacts that of the caregiver and vice versa.49 The survivor’s and 
caregiver’s distress exhibit similar trajectories. Therefore, the second intervention tested in sequencing is the 
8-week telephone interpersonal counseling intervention (TIP-C) to manage psychological distress of the 
dyad.8,50 This project will determine which dyads require which intervention sequence: SMSH alone, SMSH 
alone stepped up with TIP-C based on demonstrated needs after giving SMSH alone 4 weeks of time, or a 
combined TIP-C+SMSH for the first 8 weeks then SMSH alone for 4 weeks. Dyads will be initially randomized 
to either SMSH alone or TIP-C+SMSH (Figure 1). If the survivor’s depression or anxiety does not respond to 
SMSH alone at week 4, dyads will be re-randomized to the TIPC+ SMSH or continue with SMSH alone. 
Outcome data will be collected at baseline, weeks 13 (post-intervention) and 17 (follow-up). Brief assessments 
during weeks 1-12 will track any change in the dyad’s symptoms, intervention receipt, enactment and fidelity. 
Assessments and interventions are telephone-delivered in English or Spanish based on participant preference, 
as done in past studies.8,50,51 
 
Design. We selected the SMART design (Figure 1) for this study over alternative designs (e.g., 
implementation designs) because the SMART design allows a precision or personalized approach to 
determine the right treatment at the right dose with the right sequence for the right survivor-caregiver dyad. 
SMART designs, although newer, show promise in developing the sequences of evidence-based interventions 
for more efficient and individualized patient- and caregiver-centered care. We will use findings from this study 
to create an algorithm for clinically meaningful decision making about symptom management for survivors and 
their caregivers to be tested in future implementation/dissemination studies. We will recruit 298 cancer 
survivors undergoing chemotherapy, targeted therapy, or hormonal therapy for a solid tumor at the NCI 
designated University of Arizona Comprehensive Cancer Center (UACC, Tucson and Phoenix locations) and 
at Arizona community oncology settings. The survivors will be screened for moderate or severe depression 
and/or anxiety prior to enrollment and identify the informal caregivers who will participate in the study. 
Following enrollment, informed consent and baseline interview of both survivor and caregiver, the dyad will be 
randomly assigned to either: 1) SMSH alone or 2) TIP-C+SMSH for 8 weeks followed by continued SMSH 
alone for 
4 weeks. During 12 weeks following initial randomization, all participants will receive weekly telephone contacts 
to assess symptoms, deliver the assigned intervention and assess its enactment and fidelity. After the initial 4 
weeks in the SMSH alone group, the survivor’s response to the intervention will be determined. If the survivor 
responds (defined as a reduced score on depression and/or anxiety) (See C9a. Primary Outcomes), the dyad 
will continue with the SMSH alone for 8 more weeks. If the survivor is a non-responder (defined as no 
improvement or a worsening score for depression and/or anxiety), the dyad will be re-randomized to either 
continue with SMSH alone for 8 more weeks, or add 8 weeks of TIP-C. The rationale for using the survivor’s 
response as the criterion for re-randomization is from the extensive evidence of interdependence in survivor 
and caregiver outcomes presented, and on the caregiver’s focus on the survivor’s outcomes. The rationale for 
timing of the assessment of response and re-randomization to add the TIP-C intervention after 4 weeks is 
based on past testing of the SMSH,15,16,30 where median time to response on psychological distress ranged 
from 14 to 24 days. Post-intervention and follow-up telephone assessments are at weeks 13 and 17. 
 
 

 
 
 
 
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Analytic Methods 
Data management. All data will be entered into the secure web-based database. Quarterly quality 
assurance checks of the data will be performed by the RA supervised by Dr. Sikorskii. De-identified data will 
be transferred into SAS 9.4 for analyses. The distributions of outcomes and potential covariates will be 
assessed, outliers will be investigated by inspecting the residuals, and models described below will be fit with 
and without outliers to examine their influence on the results. 
Attrition Analyses and Handling of Missing Data. We will compare dyadic characteristics of those who 
completed the study to those who did not within their designated group to inform the generalizability of findings. 
Attrition will also be compared between each pair of randomized groups. The regression techniques described 
below allow for missing at random (MAR) mechanism.264 If patterns of missing data indicate potential not 
missing at random (NMAR) mechanisms, then models describing missing mechanisms will be considered 
(e.g., pattern-mixture models),265,266 and sensitivity analyses will investigate the robustness of the results. 
Primary Analysis. Primary analyses will follow the intent to treat approach. 
Aim 1, Hypothesis 1: Dyads initially randomized to the TIP-C+SMSH will report lower depression, anxiety, 
and 
summed severity of other symptoms at weeks 1-12, 13, and 17, and lower unscheduled health services use, 
higher self-efficacy and social support, and lower caregiver burden at weeks 13 and 17 as compared to those 
initially randomized to the SMSH alone. This hypothesis will be tested using statistical model #1 that relates 
repeated measures of the survivor or caregiver outcome y (one at a time) to the group assignment variable 
𝑥𝑥1, outcome at baseline 𝑥𝑥2, time entered as a class variable to model potentially non-linear patterns, and 
other covariates. For normally distributed outcomes, this model will be fit as a linear mixed effects model 
(LME). Generalized linear mixed effects (GLME) modeling will be used if outcome is not normally distributed 
and cannot be normalized using transformations. For health service use, statistical model #1 will be 
implemented as a GLME model with Poisson distributed errors, or as a zero-inflated Poisson or negative 
Binomial model based on the distribution of the counts of different health services uses. Each type of health 
services use (e.g., hospitalizations, ED visits) will be analyzed separately. The explanatory variables including 
study group will be evaluated as predictors of zero inflation (whether or not the count is zero), and also as 
predictors of the magnitude of the count when it is not zero. The main effects of the group variable 𝑥𝑥1will be 
tested. 
Aim 2, Hypothesis 2: Dyads where survivors do not respond to the SMSH alone during weeks 1-4 and have 
TIP-C added during weeks 5-12, will report lower depression, anxiety, and summed severity of other symptoms 
at weeks 5-12, 13, and 17, and lower unscheduled health services use, higher self-efficacy and social support, 
and lower caregiver burden at weeks 13 and 17 as compared to those who are re-randomized to continue with 
the SMSH alone. The strategy described under the analyses for Aim 1 will be implemented for the repeated 
outcome measures during weeks 5-12 and weeks 13 and 17 that will be related to group assignment from the 
second randomization, symptom severity during week 4, time, and covariates. 
Mediation analyses for Aims 1 and 2. To test for mediation, the study group will be treated as the 
independent variable, and each of the potential mediators (one at a time) will be tested for their effect on the 
symptom outcome variable at weeks 13 and 17, with the baseline measure of that respective symptom 
outcome variable treated as a covariate. Caregiver burden will be tested only at the individual level, but social 
support and self-efficacy will be tested at the individual and dyadic levels. We will use a bias corrected 
bootstrapping analytic strategy267,268 based on 5000 bootstrap samples to estimate confidence intervals around 
the indirect effect of study group on the outcome variable, through the mediator. To establish mediation, the 
95% confidence interval around the indirect effect must not include 0. 
Aim 3 examines the dyadic interdependence in outcomes between survivors and caregivers. This 
interdependence will be modeled and tested with the actor-partner interdependence mediation model 
(APIMem)269 in structural equation modeling. The APIMem estimates three classes of effects: actor effects 
(e.g., person A independent variable (IV)→person A dependent variable (DV)), caregiver (partner) effects (e.g., 
person A IV→person B DV), and mediation effects (e.g., person A IV→ person B Mediator→ person A DV) in 
an omnibus model. These models will specify correlations between the survivors’ and caregivers’ IVs as well 
as covariances between the error terms of the mediators and outcome variables, recognizing that these 
residuals will covary between dyad members due to unmeasured common causes. We will fit both a saturated 
version of the model in which all actor, partner, and mediation effects are free to vary and compare that with a 

 
 
 
 
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constrained model in which the effects for one dyad member are constrained equal to the corresponding 
effects of the other dyad member. The χ2 difference test will determine whether the more parsimonious 
constrained model or the unconstrained model will be interpreted. This test will also indicate whether the effect 
for survivors is significantly different from that of caregivers. These models will test whether baseline to week 
17 changes in survivors’ outcomes of depression, anxiety and summed severity of other symptoms are 
mediated by the intervening (week 13) state or caregivers’ outcomes or potential covariates. 
Exploratory Aim 4. The dyadic characteristics of responders will be compared to those of non-responders 
using t-tests, chi-square or Fisher’s exact tests. Characteristics found to differ, along with mediators and other 
covariates listed in section C9d will be further considered as potential future tailoring variables. The decision 
rule (𝑑𝑑1, 𝑑𝑑2) specifying the first and second intervention to achieve optimal outcome will be using the 
Qlearning optimization method258,270-272 implemented in SAS PROC QLEARN.273 274 The Q-learning algorithm 
proceeds backwards from the last decision to the first. Two Q-functions will be considered. The function 
𝑄𝑄2(𝐻𝐻2) = 𝐸𝐸[𝑌𝑌2|𝐻𝐻2] is the expectation of the second stage outcome 𝑌𝑌2 given history after 2 stages, 
denoted by 𝐻𝐻2: dyadic characteristics, outcomes observed during weeks 1-12, 13 and 17, and interventions 
received. The function 𝑄𝑄1(𝐻𝐻1) = 𝐸𝐸[𝑌𝑌1 + max 𝑄𝑄2(𝐻𝐻2)] uses history through the first intervention stage 
𝐻𝐻1. The conditional expectations in the Q-functions will be estimated from regression analyses for the primary 
outcomes, and the optimal decision rules will be found using backward induction by maximizing these 
functions.275,276 The product of this analysis will be identification of tailoring variables to operationalize the 
decision rules of selecting the optimal first intervention and switching from SMSH alone to TIP-C+SMSH. 
These personalized decision rules can then undergo testing in a future confirmatory RCT. 
 
6.0  MEASURES/DATA COLLECTION INSTRUMENTS   
Describe all forms, questionnaires, instruments or other specific methods used to collect data. 
Include complete copies of all forms, interview guides, survey questionnaires, in Appendix I.  
 
Measures 
Patient Reported Outcomes Measurement Information System (PROMIS) 249 measures are suitable for both 
survivors and caregivers, have been developed using sophisticated measurement techniques, tested with over 
21,000 individuals, calibrated to produce T-scores based on the general population, and are available in either 
English or Spanish. The available short forms have evidence of good reliability and validity. 249 Our other 
measures also have good reliability (α > .80)41,84,178,205 and validity, have been translated240 and tested with 
Spanish speaking participants in our pilot studies.31  
Primary outcomes. 
Caregivers’ and survivors’ symptoms will be measured using the adapted General Symptom Distress Scale 
(GSDS),124,250 a brief instrument that allows for a quick assessment of symptoms, which is especially important 
during weekly calls. It evaluates 15 symptoms: shortness of breath, nausea, vomiting, pain, sleep difficulties, 
bowel problems, numbness or tingling, skin rashes or sores, swelling in hands and feet, difficulty concentrating, 
poor appetite, cough, depression, anxiety, fatigue. Respondents indicate presence of each symptom (yes/no) 
and rate their severity on the scale from 1 to 10. The ability to manage symptoms is also assessed on a scale 
from 1=cannot manage to 10=can manage extremely well. The GSDS has good test-retest and internal 
consistency reliability and predictive and construct validity in both English and Spanish.250 The 0-10 ratings of 
depression and anxiety and the summed severity of other 13 symptoms will be derived at each weekly contact, 
baseline, 13, and 17 week interviews. 
Survivors’ symptom response on depression and/or anxiety during weeks 1-4 as a criterion for 
rerandomization. Response will be assessed using the depression and anxiety items of the GSDS 
administered during weekly calls to survivors. Based on the inclusion criteria and established and validated 
symptom cut points, 224 survivors will enter with moderate or severe depression and/or anxiety (one symptom 
or both) . The cut-points are anchored in how much symptoms are distressing the participant by interfering with 
enjoyment of life, social relationships, and general daily activities. Participants indicate the severity/distress 
from 1 to 10. For depression, the mild category corresponds to a severity score of 1, the moderate category 
corresponds to scores 2-3, and scores of 4-10 fall into the severe category. For anxiety, the mild category is 

 
 
 
 
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severity of 1-3, the moderate category corresponds to scores 4-5, and the severe category is 6-10. Survivors 
who start at severe or moderate on depression and/or anxiety symptoms at intake and end at a lower category 
by the week 4 observation will be called responders to the intervention.228 If a symptom was mild at intake, 
symptom response would not be applicable. Because responders demonstrate substantial improvement 
anchored to symptom distress after 4 weeks, responders will continue with the SMSH only intervention for 
another 8 weeks. Non-responders to the intervention are survivors who do not respond on either or both 
symptoms.224,251,252 Non-responding survivors and their caregivers will be re-randomized to either continue with 
the SMSH alone for 8 weeks to give it additional time or add TIP-C for 8 weeks to rigorously test the value 
added by the more intensive intervention in Aim 2. Total intervention time is 12 weeks. 
Caregivers’ and survivors’ depression and anxiety. PROMIS-short forms 8: depression and anxiety253-256 will be 
administered at baseline, 13 and 17 week telephone interviews to provide greater detail and precision in the 
measurement of these outcomes, as compared to single GSDS items administered in weekly calls. We chose 
8-item short forms to minimize respondent burden while maintaining measurement precision. 
Secondary outcomes. Secondary outcomes are caregivers’ and survivors’ hospitalizations, urgent care or 
ED visits during the study. In baseline, week 13 and 17 interviews over the telephone, each dyad member will 
be asked to recall ED visits and admissions to hospitals and, if they occurred, their reasons and duration. 
Recall period will be 3 months in baseline and week 13, and 1 month in week 17 interviews. Extensive 
previous research2-5 documents self-report is a reliable and valid method to collect health services use data 
especially when standardized methods are used and the recall period is short, as in this project. Self-report is 
the only reasonable and cost-effective way to assess healthcare use, as it would be impossible to access 
health (medical) records across the multiple systems and payers used by participants in this study. 
Potential mediators. 
Caregivers’ and survivors’ self-efficacy. PROMIS 4-item short forms (sf) will be administered in interviews.253-256 
Self-efficacy specific to symptom management will be captured by the GSDS item described above. 
Caregivers’ and survivors’ social support. PROMIS 4-item-sf for emotional support and PROMIS Informational 
Support 4a will be used in interviews.253-256 
Caregiver Reaction Assessment Tool.257 This caregiver burden tool was developed and validated with 
caregivers of patients with chronic conditions. It has 5 subscales: impact on schedule, caregiver’s esteem, 
family support, impact on health, and impact on finances, with Cronbach’s alphas exceeding 0.70. 
Potential Covariates and Future Tailoring Variables. These variables will be assessed during baseline 
interviews of survivors and caregivers. Demographic characteristics include caregivers’ and survivors’ age, 
education, work, ethnicity, race, acculturation, marital status, relationship between survivor and the caregiver, 
and living arrangement. Comorbidity will be measured with the Bayliss tool that queries the presence of 20 
comorbidities,258 and we will also collect height and weight to calculate BMI. The validity and reliability of self-
reported height and weight are adequate259-261, and health risk estimates associated with BMI values are 
virtually the same, whether based on self-report or measured BMI values.262 Caregiver’s activities of survivor 
care will be measured using a checklist19, and quality of relationship will be measured using a 6-item index 
designed to assess the relationship quality. The index has established reliability with samples of married 
couples263 and has also been used to capture survivors’ perceptions of the quality of relationship with their 
friend/family caregiver. Preferred language of intervention delivery will be tracked. Receipt and enactment of 
intervention strategies are measured during weeks 1-12. Receipt will be measured by the number of completed 
weekly sessions. Enactment of the SMSH strategies is assessed at the beginning of calls during weeks 2-12 
as described in section C8c. Enactment of the TIP-C will be measured by tracking the implementation of 
behaviors discussed and completion of the assigned homework as documented in counselor’s field notes for 
each session. Assessment of survivors’ radiation, surgery, chemotherapy, targeted or hormonal therapy (dose, 
type, dates received), cancer site and stage, and medications (e.g., supportive agents for symptoms) will be 
collected from health record data corresponding with the time-on-study. Every effort has been made to keep 
respondent burden to a minimum and to distribute any burden over the course of the study. If needed, we can 
divide the assessments into two sessions over two days. Yet, few participants requested such 
accommodations in past studies. 
 
 
7.0 
DETAILED DESCRIPTION OF INTERVENTION 

 
 
 
 
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Describe in detail for intervention studies or indicate otherwise by checking below:  
 
Interventions. We deliver interventions via the telephone (see section C2e for rationale) at convenient times 
for both the survivor and caregiver, including evenings and weekends. 
Symptom Management and Survivorship Handbook (SMSM). is an evidence-based self-care management 
guide specific to each symptom.84-87 Each module is presented in an identical format (frequently asked 
questions): what the symptom is, how people describe the symptom, the causes of the symptom including 
medications, and a set of 
strategies presented in bullet points for managing the symptom. For each symptom, there are indications as to 
when and for what reasons to contact the oncology practice; other resources for management are listed. The 
previously tested English version will be translated into Spanish using an adaptation of Brislin’s 
translation/back translation process240 used by this team in the past. Professor Jaime Fatás-Cabeza, Director 
of the Undergraduate Translation and Interpretation Program at the University of Arizona, will oversee the 
translation. Cultural experts will perform back translations of a random sample of pages from the SMSH for 
comparison to the original English language versions, and all discrepancies corrected between the back 
translated and original English language pages. Finally, a focus group of six Spanish-speaking Latinos will 
discuss the translation in terms of understandability (language level and complexity), use of idioms, and 
consistency of meaning. Focus group data will be used to finalize the SMSH translation and layout (i.e. 
design). 
Survivors and caregivers will be mailed the Handbook in the participant’s preferred language following the 
baseline interviews. During each week, staff will call the survivors and then their caregivers. The call will begin 
with the assessment of symptoms using the General Symptom Distress Scale (GSDS, described in measures). 
For each symptom rated at 4 or higher on a 0-10 scale of severity, the survivors will be referred to the SMSH 
for 
symptom self-management. The threshold of 4 was selected based on the NCCN guidelines for symptom 
monitoring and management83 and used successfully in past work.75,76,81,82,89 During weeks 2-12, the survivor’s 
calls will begin with assessing SMSH use since the last call (intervention enactment), followed by the 
administration of the GSDS and referral to the SMSH. During weekly calls to caregivers, symptoms will also be 
assessed using GSDS. The caregivers will be notified of any current symptoms above threshold experienced 
by survivors and directed to the SMSH to assist the survivors in intervention enactment. Sharing symptom 
information between survivor and caregiver will be part of the informed consent. During weeks 2-12, the 
caregiver’s calls will begin with assessing SMSH use for the management of survivors’ symptoms, followed by 
the GSDS, summary of survivors’ symptoms and referral to the SMSH. Calls will last about 10 minutes. 
 
 
Telephone Interpersonal Counseling Intervention (TIP-C). Social work counselors with a master’s 
degree and behavioral health and oncology expertise will deliver the 8-week TIP-C intervention (Table 2). 

 
 
 
 
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During weekly contacts, the counselors target social support behaviors using interpersonal communications 
techniques. Interpersonal communication facilitates processing stressful affective reactions to a cancer 
diagnosis and treatment, marshalling instrumental support for assistance with roles and functions, 
informational support for advice and information, and appraisal support for gauging and adjusting to the 
stressor. Counselors can personalize the counseling intervention for the specific needs or interests as 
expressed during sessions while still adhering to a structured protocol. For example, one survivor may need to 
focus on depression and family issues (e.g., role transitions such as job loss) rather than on anxiety and 
resource issues (e.g., transportation, lack of insurance). This approach is consistent with survivorship care 
recommendations241 and recent evidence showing that improved psychological well-being occurs when an 
intervention addresses practical resource needs.242 
 
Each survivor and caregiver receiving the TIP-C+SMSH intervention will receive one 40-minute 
telephone call per week for 8 weeks (8 sessions). The first 10 minutes of the call will follow the SMSH only 
intervention procedures (see C8c). The next 30 minutes will be devoted to the delivery of TIP-C. The TIP-C 
sessions will incorporate the symptoms assessment performed at the beginning of the call as follows: 
discussion of depression, anxiety and stress per protocol and referral to the SMSH for symptoms. The final 4 
weeks will be SMSH only. The TIP-C intervention protocol is the same for both survivor and caregiver, the 
same counselor will be assigned to both members of the dyad and sessions conducted in either Spanish or 
English. 
Counselors call the survivor and caregiver at separate convenient times to ensure they have adequate time 
and privacy to participate. Numerous interventions for cancer survivorship use individually delivered methods 
243-245 as we will use in this study. Dyadic delivery (i.e., both present at the same time) is not required and 
separate delivery resolves two major obstacles associated with delivering TIP-C to both members 
simultaneously. 1) Participants may be unwilling to discuss certain issues when the other dyad member is 
present such as discussing concerns that they have about each other. In such cases, the counselor can be an 
effective bridge between the two. Other times, participants may wish to discuss personal concerns (e.g., 
survivor dying). 2) Scheduling and technological difficulties multiply when both members must speak on the 
phone with a third party at the same time. 
Training and intervention fidelity. Intervention protocol fidelity will be assured using established methods 
outlined by the NIH Treatment Fidelity Workgroup on consistency in dose, providers, delivery, and receipt of 
the intervention.246 TIP-C interveners will receive 24 hours of education, augmented by additional books and 
articles, about cancer diagnosis and treatment, psychological distress, and interpersonal counseling 
techniques with training protocols from previous studies.41,205,209,247 Counselors will listen to 8-10 hours of 
counseling sessions recorded for training purposes. Drs. Badger and Segrin will conduct training that will 
continue until the counselor is rated as achieving > 90% on protocol implementation. Annual re-training occurs 
throughout the study. 
The intervention fidelity protocols used in past studies will be used in this study. All sessions will be digitally 
recorded and about 10% randomly reviewed throughout the study to maintain quality, with written and 
verbal feedback given to the counselors. Drs. Badger and Segrin will supervise the intervention quality control 
activities. Through weekly case supervision, we will maintain fidelity of the intervention and counselor 
adherence to protocols. We will evaluate adherence (number required elements discussed/ total number of 
elements).142,153,206 Drs. Badger and Segrin will listen to all sessions in English from the first 5 dyads (40 hours 
of supervision) and then randomly review 10% of sessions throughout the study. A bilingual counselor will 
review sessions in Spanish using established protocols as in past studies. No one with less than 90% 
adherence will receive new cases until retraining has occurred, and Drs. Badger or Segrin will assume 
responsibility for those existing cases. Following retraining, 5 dyads will be monitored to ensure that >90% 
adherence is achieved and then we will return to randomly selected monitoring for quality control. Anyone 
unable to adhere to the standardized protocols after a second retraining will be replaced. 
Intervention reproducibility. Interventions must be standardized, yet the complexities of symptom distress 
demand a flexible approach to preserve the relevance of TIP-C for the individual. We will determine the 
amount of elements personalized to the specific needs of the individual within the structured protocol (number 
of personalized elements/ total number of elements). We will then examine the effect of personalization (e.g., 
more discussion of socioeconomic needs with one participant vs. another), if any, on outcomes. Counselors 
will keep detailed field notes after each session assessing intervention length, rapport, responsiveness, topics 
discussed, homework completed and satisfaction. Our past adherence rate of >85% far exceeds the rate 

 
 
 
 
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reported for community mental health patients who return for face-to-face appointments.248 Participants who 
miss sessions (occurrence is rare) will be rescheduled, as we will obtain multiple points of contact (e.g., home, 
cell, work telephone, e-mail address). If we fail to contact within the week, we will schedule the following week. 
We will also send an e-mail, text, or letter asking the participant to call us. Attrition rates and reasons will be 
documented, including being unable to reach the survivor or the caregiver or expressed desire to discontinue. 
 
 
 
 
8.0 
STATISTICAL CONSIDERATIONS 
Analysis Plan for answering objectives including endpoint definitions, patient accrual 
objectives, and estimated duration of study. This section should be developed in consultation 
with appropriate biostatistician. 
 
 
Potential Covariates and Future Tailoring Variables. These variables will be assessed during baseline 
interviews of survivors and caregivers. Demographic characteristics include caregivers’ and survivors’ age, 
education, work, ethnicity, race, acculturation, marital status, relationship between survivor and the caregiver, 
and living arrangement. Comorbidity will be measured with the Bayliss tool that queries the presence of 20 
comorbidities,258 and we will also collect height and weight to calculate BMI. The validity and reliability of self-
reported height and weight are adequate259-261, and health risk estimates associated with BMI values are 
virtually the same, whether based on self-report or measured BMI values.262 Caregiver’s activities of survivor 
care will be measured using a checklist19, and quality of relationship will be measured using a 6-item index 
designed to assess the relationship quality. The index has established reliability with samples of married 
couples263 and has also been used to capture survivors’ perceptions of the quality of relationship with their 
friend/family caregiver. Preferred language of intervention delivery will be tracked. Receipt and enactment of 
intervention strategies are measured during weeks 1-12. Receipt will be measured by the number of completed 
weekly sessions. Enactment of the SMSH strategies is assessed at the beginning of calls during weeks 2-12 
as described in section C8c. Enactment of the TIP-C will be measured by tracking the implementation of 
behaviors discussed and completion of the assigned homework as documented in counselor’s field notes for 
each session. Assessment of survivors’ radiation, surgery, chemotherapy, targeted or hormonal therapy (dose, 
type, dates received), cancer site and stage, and medications (e.g., supportive agents for symptoms) will be 
collected from health record data corresponding with the time-on-study. Every effort has been made to keep 
respondent burden to a minimum and to distribute any burden over the course of the study. If needed, we can 
divide the assessments into two sessions over two days. Yet, few participants requested such 
accommodations in past studies. 
 
Scientific Rigor and Transparency. The scientific rigor of this study is ensured by the randomized design, 
complete inclusion/exclusion criteria defining the population to which findings would be generalizable, 
reproducible manualized protocol for the interventions, tracking of intervention fidelity, dose, receipt and 
enactment, use of measures with solid evidence of reliability and validity, blinding of data collectors, 
transparent assessment and statistical analysis plans including attention to biases and the missing data. 
 
Sex as a biological variable. We will consider survivors’ and caregivers’ sex as covariates in all analyses. 
Past research indicates that the survivor-caregiver relationship (spouse/partner versus other) is a 
key factor that may influence outcomes62,165 for the dyad over and above sex. Relationship will be controlled in 
randomization and considered along with sex as a covariate and potential future tailoring variable. 
 
Analytic Methods 
Data management. All data will be entered into the secure web-based database. Quarterly quality 
assurance checks of the data will be performed by the RA supervised by Dr. Sikorskii. De-identified data will 
be transferred into SAS 9.4 for analyses. The distributions of outcomes and potential covariates will be 
assessed, outliers will be investigated by inspecting the residuals, and models described below will be fit with 
and without outliers to examine their influence on the results. 

 
 
 
 
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Attrition Analyses and Handling of Missing Data. We will compare dyadic characteristics of those who 
completed the study to those who did not within their designated group to inform the generalizability of findings. 
Attrition will also be compared between each pair of randomized groups. The regression techniques described 
below allow for missing at random (MAR) mechanism.264 If patterns of missing data indicate potential not 
missing at random (NMAR) mechanisms, then models describing missing mechanisms will be considered 
(e.g., pattern-mixture models),265,266 and sensitivity analyses will investigate the robustness of the results. 
Primary Analysis. Primary analyses will follow the intent to treat approach. 
Aim 1, Hypothesis 1: Dyads initially randomized to the TIP-C+SMSH will report lower depression, anxiety, 
and 
summed severity of other symptoms at weeks 1-12, 13, and 17, and lower unscheduled health services use, 
higher self-efficacy and social support, and lower caregiver burden at weeks 13 and 17 as compared to those 
initially randomized to the SMSH alone. This hypothesis will be tested using statistical model #1 that relates 
repeated measures of the survivor or caregiver outcome y (one at a time) to the group assignment variable 
𝑥𝑥1, outcome at baseline 𝑥𝑥2, time entered as a class variable to model potentially non-linear patterns, and 
other covariates. For normally distributed outcomes, this model will be fit as a linear mixed effects model 
(LME). Generalized linear mixed effects (GLME) modeling will be used if outcome is not normally distributed 
and cannot be normalized using transformations. For health service use, statistical model #1 will be 
implemented as a GLME model with Poisson distributed errors, or as a zero-inflated Poisson or negative 
Binomial model based on the distribution of the counts of different health services uses. Each type of health 
services use (e.g., hospitalizations, ED visits) will be analyzed separately. The explanatory variables including 
study group will be evaluated as predictors of zero inflation (whether or not the count is zero), and also as 
predictors of the magnitude of the count when it is not zero. The main effects of the group variable 𝑥𝑥1will be 
tested. 
Aim 2, Hypothesis 2: Dyads where survivors do not respond to the SMSH alone during weeks 1-4 and have 
TIP-C added during weeks 5-12, will report lower depression, anxiety, and summed severity of other symptoms 
at weeks 5-12, 13, and 17, and lower unscheduled health services use, higher self-efficacy and social support, 
and lower caregiver burden at weeks 13 and 17 as compared to those who are re-randomized to continue with 
the SMSH alone. The strategy described under the analyses for Aim 1 will be implemented for the repeated 
outcome measures during weeks 5-12 and weeks 13 and 17 that will be related to group assignment from the 
second randomization, symptom severity during week 4, time, and covariates. 
Mediation analyses for Aims 1 and 2. To test for mediation, the study group will be treated as the 
independent variable, and each of the potential mediators (one at a time) will be tested for their effect on the 
symptom outcome variable at weeks 13 and 17, with the baseline measure of that respective symptom 
outcome variable treated as a covariate. Caregiver burden will be tested only at the individual level, but social 
support and self-efficacy will be tested at the individual and dyadic levels. We will use a bias corrected 
bootstrapping analytic strategy267,268 based on 5000 bootstrap samples to estimate confidence intervals around 
the indirect effect of study group on the outcome variable, through the mediator. To establish mediation, the 
95% confidence interval around the indirect effect must not include 0. 
Aim 3 examines the dyadic interdependence in outcomes between survivors and caregivers. This 
interdependence will be modeled and tested with the actor-partner interdependence mediation model 
(APIMem)269 in structural equation modeling. The APIMem estimates three classes of effects: actor effects 
(e.g., person A independent variable (IV)→person A dependent variable (DV)), caregiver (partner) effects (e.g., 
person A IV→person B DV), and mediation effects (e.g., person A IV→ person B Mediator→ person A DV) in 
an omnibus model. These models will specify correlations between the survivors’ and caregivers’ IVs as well 
as covariances between the error terms of the mediators and outcome variables, recognizing that these 
residuals will covary between dyad members due to unmeasured common causes. We will fit both a saturated 
version of the model in which all actor, partner, and mediation effects are free to vary and compare that with a 
constrained model in which the effects for one dyad member are constrained equal to the corresponding 
effects of the other dyad member. The χ2 difference test will determine whether the more parsimonious 
constrained model or the unconstrained model will be interpreted. This test will also indicate whether the effect 
for survivors is significantly different from that of caregivers. These models will test whether baseline to week 
17 changes in survivors’ outcomes of depression, anxiety and summed severity of other symptoms are 
mediated by the intervening (week 13) state or caregivers’ outcomes or potential covariates. 

 
 
 
 
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Exploratory Aim 4. The dyadic characteristics of responders will be compared to those of non-responders 
using t-tests, chi-square or Fisher’s exact tests. Characteristics found to differ, along with mediators and other 
covariates listed in section C9d will be further considered as potential future tailoring variables. The decision 
rule (𝑑𝑑1, 𝑑𝑑2) specifying the first and second intervention to achieve optimal outcome will be using the 
Qlearning optimization method258,270-272 implemented in SAS PROC QLEARN.273 274 The Q-learning algorithm 
proceeds backwards from the last decision to the first. Two Q-functions will be considered. The function 
𝑄𝑄2(𝐻𝐻2) = 𝐸𝐸[𝑌𝑌2|𝐻𝐻2] is the expectation of the second stage outcome 𝑌𝑌2 given history after 2 stages, 
denoted by 𝐻𝐻2: dyadic characteristics, outcomes observed during weeks 1-12, 13 and 17, and interventions 
received. The function 𝑄𝑄1(𝐻𝐻1) = 𝐸𝐸[𝑌𝑌1 + max 𝑄𝑄2(𝐻𝐻2)] uses history through the first intervention stage 
𝐻𝐻1. The conditional expectations in the Q-functions will be estimated from regression analyses for the primary 
outcomes, and the optimal decision rules will be found using backward induction by maximizing these 
functions.275,276 The product of this analysis will be identification of tailoring variables to operationalize the 
decision rules of selecting the optimal first intervention and switching from SMSH alone to TIP-C+SMSH. 
These personalized decision rules can then undergo testing in a future confirmatory RCT. 
 
Potential Difficulties/Limitations and Alternative Approaches 
Potential problems from recruitment and retention will be minimized by the 
use our previous methods yielding high retention rates with no differential attrition between conditions. 
Potential problems in intervention delivery will be minimized by implementing protocols to maintain intervention 
fidelity. There are no high-risk aspects of this trial, and all procedures are non-invasive. We recognize that in 
addressing depression and anxiety our efforts might inadvertently produce detrimental psychological 
responses. Should this occur, our experienced interventionists will refer the survivor and/or caregiver to mental 
health services. Because randomizations may not account for all possible error sources, we will adjust for 
baseline values of outcomes in the analysis to provide added control over possible confounding preintervention 
influences. Three primary outcomes (depression, anxiety, and summed severity of other symptoms) and all 
hypotheses are stated a priori. In the exploratory analyses, the Benjamini-Hochberg or Hochberg 
adjustment277-279 will be used to control the false discovery rate. 
 
 
 

 
 
 
 
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9.0 
STUDY TIMELINE 
 
List of all parameters and required intervals for observations, measurement of outcomes, 
intervention and intervals at which it is given.   
 
Table 3 represents the timeline of the project over the 4 years. Table 4 represents the specific schedule 
of events for the project. 
 
 
     
 
 
10.0 DATA SAFETY AND MONITORING PLAN 
 
Describe plans to monitor adherence to study protocol, integrity of data collection and 
intervention delivery.  Include any plans regarding project quality assurance. 
 
Data Safety and Monitoring Plan. This behavioral intervention study meets the definition of a clinical 
trial. Data and Safety Monitoring will be accomplished through multiple approaches: 
Institutional Review Board (IRB) - All procedures related to data collection and safety will be approved 
by the University of Arizona (UA) Health Sciences Center IRB and by the independent Arizona Cancer 
Center Office of Clinical Trials. Processes will be established to guide collection, transfer and storage of 
data and training of staff to ensure data safety. Quality assurance (QA) reviews of data and staff will be 
performed as described below. 
 

 
 
 
 
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1. Security Procedures for Collection, Transfer and Storage of Electronic Data. Electronic files will 
consist of enrollment data, survey data at two time points, symptom data collected during the weekly 
calls, interventionist call data for survivors allocated to TIP-C + SMSH and survivor’s medical record 
data (collected at enrollment and during medical record audit after survivors complete their 13 week 
interviews). First, enrollment data with identifiers will be stored separately from the study data. Second, 
all computers that will store data at the central location will be password protected. The system will 
have a secure login along with audit control mechanisms to meet the HIPAA guidelines. Third, servers 
will utilize state-of-the art security processes. Electronic copies of forms, such as the consent or HIPAA 
authorization form will be stored on a secure dedicated server with appropriate firewalls. The system 
will use encryption (SSL certificate) to transfer data between the machines. This technology is the same 
as that used for online e-commerce applications to protect consumer information. Servers are scanned 
for viruses and systems are in-place to detect attempts at unauthorized entry. All transactions to the 
database are stored in archive logs as re-do data and are accessible to enable quick recovery of all 
data should the need arise. Backup files are written nightly to back up servers. 
 
2. Security Procedures for Collection, Transfer and Storage of Paper Data. Paper files will consist of 
consent and authorization forms and medical record audits. Paper copies of all forms will be faxed to 
the central study office for data entry. Faxed copies of medical records will be retained in locked 
storage cabinets at UA accessible only to study personnel. 
 
3. Training of Staff.  
Recruiters- will follow UA institutional processes for enrollment of patients to clinical trial(s). Training by 
the study Coordinator will occur in order to ensure recruiters understand eligibility criteria, study design 
and goals. Training will emphasize strategies to maximize enrollment and retention of minority 
participants. Additionally, training will occur to assure recruiters understand the function and importance 
of data gathered during the medical record audit at enrollment. Training will include completion of 
simulated cases. Booster training sessions will be scheduled as needed. 
Interviewers/Data Collectors - will be carefully instructed and trained in appropriate interviewing 
techniques and will receive regular monitoring by the study Coordinator to ensure the ethics of research 
and scientific integrity and protection of confidentiality. Participants are asked prior to each interview if 
they want to continue and are given a toll-free number to contact UA if they have questions or 
concerns. 
Interventionists- TIP-C interventionists will receive 24 hours of education, augmented by additional 
books and articles, about cancer diagnosis and treatment, psychological distress, and interpersonal 
counseling techniques with training protocols developed in previous studies (Appendix C). The 
interventionists will listen to 8-10 hours of counseling sessions recorded for training purposes. Drs. 
Badger and Segrin will conduct interventionist training that will continue until the interventionists are 
rated as achieving > 90% on protocol implementation. Annual re-training will occur throughout the 
study. 
Medical Record Auditors – will be the recruiters or are employees of each recruitment site with 
oncology experience but trained by the study Coordinator on collection of data for this study. 
Training will target job descriptions, and roles and responsibilities of group members and will consist of 
1) an overview of project objectives, theoretical framework, and research design and rationale, 2) 
background and training on collecting data free from bias, 3) information on scale and item response 
issues; 4) protection of human patients and confidentiality issues; and 5) data and intervention 
monitoring and quality assurance procedures. Activities for training will consist of lectures, discussion, 
 
 
4. Monthly Meetings – Monthly meetings with all research staff will be conducted to review accrual, 
attrition, discuss problems and/or concerns and ensure everyone understands and is following the 
protocol. 
5. Quality Assurance Activities for Project Staff – Quarterly quality assurance (QA) will involve engaging 
in good data management activities. Procedures that include checking the integrity of data storage and 
examining frequency distributions to look for anomalies such as an excessive number of “don’t know” 
responses or problems with skip patterns will be in place. 

 
 
 
 
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Recruiters – Enrollment data will be monitored monthly for completeness and consistency. If any 
missing data are identified, the completion of missing fields will be requested and questions clarified 
during quarterly QA review of the data. 
 
Interviewers - The level of quality of each interviewer and the interview process are monitored monthly 
by the study Coordinator and Investigators. A cadre of well-trained interviewers at UA is available and 
used by this team in previous studies. Early in the study, until proficiency is reached, the interviewers 
digitally record every interview for QA. Following initial training, interviewers will be required to record 
and submit 1 interview each month for review. There is no identifying information recorded. Booster 
training sessions are held with interviewers on a scheduled basis. Written feedback on the quality of the 
telephone interview is provided to all interviewers following review of each recording. 
Interventionists - All sessions are digitally recorded and about 10% randomly reviewed throughout the 
study to maintain quality, with written and verbal feedback given to the counselors. Drs. Badger and 
Segrin will supervise the intervention quality control activities. Through weekly case supervision, we will 
maintain fidelity of the intervention and counselor adherence to protocols. We will evaluate adherence 
(number required elements discussed/ total number of elements). Drs. Badger and Segrin will listen to 
all sessions in English from the first 5 dyads (40 hours of supervision) and then randomly review 10% of 
sessions throughout the study. A bilingual counselor will review sessions in Spanish using established 
protocols as in past studies. Counselors who do not maintain 90% adherence will not be given new 
cases until retraining has occurred, and Drs. Badger or Segrin will assume responsibility for those 
existing cases. Following retraining, 5 dyads will be monitored to ensure that >90% adherence is 
achieved and then we will return to randomly selected monitoring for quality control. Anyone unable to 
adhere to the standardized protocols is replaced after a second retraining. 
 
Data - Quality assurance reports will be prepared on a quarterly basis by the statistical research 
assistant supervised by Dr. Sikorskii and reviewed by the study Coordinator and investigative team. 
That is, for the internal audit, someone independent of the data collector will check the data. Data from 
10% of the recorded interviews and intervention sessions compared to database entries. The 
acceptable error rate is 0.3 %, i.e., 3 out of 1,000 fields. All errors are corrected during the QA check. 
Dr. Sikorskii will oversee preparation of the data report distributed to all investigators at least 5 days 
before the scheduled meeting. The report will include the summary of cumulative and quarterly accrual, 
randomization, cumulative attrition, and attrition by study group, gender, and race/ethnicity, adverse 
events and serious adverse events, data completeness and quality, and study Consolidated Standards 
of Reporting Trials (CONSORT) chart. Reports will also inform the Investigators about missing, invalid, 
or inconsistent data on selected key variables. For the external oversight, Dr. Jessica Rainbow who is 
not involved in the study has agreed to participate in QA meetings. 
 
6. Identification of Adverse Effects- The following will be considered serious adverse events (SAE): 
death, attempted suicide, major depression, breach of confidentiality. Death, attempted suicide and 
major depression would not occur as a direct result of study interventions, however, could be 
encountered during implementation of this study due to the inclusion criteria of cancer survivors. A 
breach in confidentiality may result from participation in this study. The investigators have successfully 
trained staff to monitor and protect confidentiality of participants in large research studies conducted 
over the past two decades. Similar training strategies will be incorporated for training of staff in this 
study. Additionally, all research staff will complete the human subjects and HIPAA certification training. 
 
The following will be considered adverse events (AE): severe symptoms requiring hospitalization or 
urgent care. Again, severe symptoms are not expected to result from participation in this study but may 
result from cancer, its treatment or other existing comorbid conditions. 
 
Averse events and serious adverse events may be identified during implementation of the experimental 
protocols and are monitored by the Investigators in several ways. 
Interviewers: Interviewers may identify both serious adverse events and/or adverse events 

 
 
 
 
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during completion of telephone interviews or telephone calls to schedule telephone interviews. 
Interventionists: The interventionists may identify both serious adverse events and/or adverse 
events during their telephone contacts with the participants. 
 
13.0 
REFERENCES 
1. Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2017. CA: a cancer journal for clinicians. 2017; 
67:7-30. 
2. Butler AC, Chapman JE, Forman EM, Beck AT. The empirical status of cognitive-behavioral therapy: 
a review of meta-analyses. Clinical psychology review. 2006;26(1):17-31. 
3. Given C, Given B, Rahbar M, et al. Effect of a cognitive behavioral intervention on reducing symptom 
severity during chemotherapy. Journal of Clinical Oncology. 2004;22(3):507-516. 
4. Sikorskii A, Given C, Given B, Jeon S, Decker V, Decker D. Symptom management for cancer 
patients: A trial comparing two multimodal interventions. Journal of Pain and Symptom Management. 
2007;34(3):253-264. 
5. Northouse LL, Mood DW, Schafenacker A, et al. Randomized clinical trial of a family intervention for 
prostate cancer patients and their spouses. Cancer. 2007;110(12):2809-2818. 
6. Ganz P, Kwan L, Stanton A, Krupnick J, Rowland JH, Meyerowitz B. Quality of life at the end of 
primary treatment of breast cancer: First results from the moving beyond cancer randomized trial. 
Journal of the National Cancer Institute. 2004;96(5):376-387. 
7. Stanton AL, Ganz PA, Kwan L, et al. Outcomes from the Moving Beyond Cancer psychoeducational, 
randomized, controlled trial with breast cancer patients. Journal of clinical oncology : official journal of 
the American Society of Clinical Oncology. 2005;23(25):6009-6018. 
8. Huang H, Chen M, Liang J, Miaskowski C. . Changes in and predictors of severity of fatigue in 
women with breast cancer: A longitudial study. International Journal of Nursing Studies. 2014:582-592. 
9. Mitchell S, Berger A. Cancer-related fatigue: the evidence base for assessment and management. . 
Cancer. 2006;12(5):374-387. 
10. Kim HJ, Malone PS, Barsevick AM. Subgroups of cancer patients with unique pain and fatigue 
experiences during chemotherapy. J Pain Symptom Manage. 2014;48(4):558-568. 
11. Mitchell A, Chan, M, Bhatti H, Halton M, Grassi L,Johansen C, Meader, N. Prevalence of 
depression, anxiety and adjustment disorder in oncological, hematological and palliative-care settings: 
a metanalysis 
of 94 interview-based studies. Lancet Oncology. 2011(12):160-174. 
12. Cleeland CS Zhao F, Chang VT, Sloan JA, O'Mara AM, Gilman PB, Weiss M, Mendoza TR, Lee 
JW, Fisch MJ. The symptom burden of cancer: Evidence for a core set of cancer-related and treatment 
related symptoms from the Eastern Cooperative Oncology Group Symptom Outcomes and Practice 
Patterns study. Cancer. 2013;119(24):4333-4340. 
13. Kim Y, Yen IH, Rabow MW. Comparing Symptom Burden in Patients with Metastatic and 
Nonmetastatic Cancer. J Palliat Med. 2016;19(1):64-68. 
14. Barsevick AM, Sweeney C, Haney E, Chung E. A systematic qualitative analysis of 
psychoeducational interventions for depression in patients with cancer. Oncol Nurs Forum. 
2002;29(1):73-84; quiz 85-77. 
15. Derogatis LR, Morrow GR, Fetting J, et al. The prevalence of psychiatric disorders among cancer 
patients. JAMA. 1983;249(6):751-757. 
16. Ganz PA. Monitoring the physical health of cancer survivors: a survivorship-focused medical 
history. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 
2006;24(32):5105-5111. 
17. Rowland JH, Hewitt M, Ganz PA. Cancer survivorship: a new challenge in delivering quality cancer 
care. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 
2006;24(32):5101-5104. 
18. Cuijpers P, Geraedts, AS, van Oppen, P, Andersson, G, Markowitz, JC, & van Straten, A. 
Interpersonal psychotherapy for depression: A meta-analysis. American Journal of Psychiatry. 
2011;168(6):581-592. 
19. Misono S, Weiss NS, Fann JR, Redman M, Yueh B. Incidence of suicide in persons with cancer. 
Journal of Clinical Oncology. 2008;26(29):4731-4738. 

 
 
 
 
Protocol Version Date:  3/3/2022 
 
 
Page 20 
 
20. Buntrock C, Berking M, Smit F, Lehr D, Nobis S, Riper H, Cuijpers P, Ebert D. Preventing 
depression in adults with subthreshold depression: Health-Economic evaluation alongside a pragmatic 
randomized controlled trial of a web-based intervention. J Med Internet Research. 2017;19(1):e5. 
21. Given CW. Automated Telephone Monitoring for Symptom Management. Funded by the NCI, # R01 
CA030724.; 2003-2009. 
22. Given B, Given C. Family Home Care for Cancer. A Community Based Model. Funded by the NCI, 
grant #R01 CA79280-09; 1997-2008. 
23. Badger T, Lazenby, M. Depression. In: Brown C, ed. A Guide to Oncology Symptom Management, 
2nd edition. Pittsburgh, PA: Oncology Nursing Society; 2015:239-264. 
24. Beckjord EB, Arora NK, McLaughlin W, Oakley-Girvan I, Hamilton AS, Hesse BW. Health-related 
information needs in a large and diverse sample of adult cancer survivors: implications for cancer care. 
J Cancer Surviv. 2008;2(3):179-189. 
25. Feuerstein M. Defining cancer survivorship. J Cancer Surviv. 2007;1(1):5-7. 
26. Hawkins NA, Soman A, Buchanan Lunsford N, Leadbetter S, Rodriguez JL. Use of Medications for 
Treating Anxiety and Depression in Cancer Survivors in the United States. J Clin Oncol 2017;35(1). 
27. Owen JE, O'Carroll Bantum E, Pagano IS, Stanton A. Randomized Trial of a Social Networking 
Intervention for Cancer-Related Distress. Annals of behavioral medicine : a publication of the Society of 
Behavioral Medicine. 2017. 
28. Hewitt M, Greenfield S, Stovall E, eds. From Cancer Patient to Cancer Survivor: Lost in Translation. 
New York, NY: The National Academies Press; 2005. 
29. Rowland JH, Yancik R. Cancer survivorship: the interface of aging, comorbidity, and quality care. J 
Natl Cancer Inst. 2006;98(8):504-505. 
30. Lethborg CE, Kissane D, Burns WI, Snyder R. Cast Adrift: the experience of completing treatment 
among women with early stage breast cancer. Journal of psychosocial oncology. 2000;18(4):73-90. 
31. Castillo PM, Herrera JL, Fernandez-Montesinos R, et al. Tiopronin monolayer-protected silver 
nanoparticles modulate IL-6 secretion mediated by Toll-like receptor ligands. Nanomedicine (Lond). 
2008;3(5):627-635. 
32. Mercadante S, Casuccio A, Fulfaro F. The course of symptom frequency and intensity in advanced 
cancer patients followed at home. J Pain Symptom Manage. 2000;20(2):104-112. 
33. Kim H, Malone PS, Barsevick AM. Subgroups of cancerp patients with unique pain and fatigue 
experiences during chemotherapy. Journal of Pain and Symptom Management. 2014;48(4):558-568. 
34. Trudel-Fitzgerald C, Savard J, Ivers H. Which Symptoms Come First? Exploration of Temporal 
Relationships Between Cancer-Related Symptoms over an 18-Month Period. Annals of behavioral 
medicine : a publication of the Society of Behavioral Medicine. 2013;45(3):329-337. 
35. Barsevick AM, Whitmer K, Nail LM, Beck SL, Dudley WN. Symptom cluster research: conceptual, 
design, measurement, and analysis issues. J Pain Symptom Manage. 2006;31(1):85-95. 
36. Barsevick A. Defining the Symptom Cluster: How Far Have We Come? Semin Oncol Nurs. 
2016;32(4):334-350. 
37. Given CW, Given B, Sikorskii A, You M, Jeon S, Champion V, McCorkle R. . Deconstruction of 
nurse delivered patient self-management interventions for symptom management: factors related to 
delivery enactment and response. . Annals of Behavioral Medicine. 2010;40(1):99-113. 
38. Badger TA, Segrin C, Figueredo AJ, Harrington J, Sheppard K, Passalacqua S, Pasvogel A, & 
Bishop M. Psychosocial Interventions to improve quality of life among prostate cancer survivors and 
their partners Quality of life research : an international journal of quality of life aspects of treatment, 
care and rehabilitation. 2011;20(6):833-844. 
39. Badger TA, Segrin C, Hepworth JT, Pasvogel A, Weihs K, Lopez AM. Telephone-delivered health 
education and interpersonal counseling improve quality of life for Latinas with breast cancer and their 
supportive partners. Psycho-oncology. 2013;22(4):1035-1042. 
40. Badger T, Segrin, C, Figueredo, AJ , Harrington, J, Sheppard, K, Passalacqua, S, Pasvogel, A, & 
Bishop, M. Who benefits from a psychosocial counseling vs. educational intervention to improve 
psychological quality of life in prostate cancer surviors. Psychology & health. 2012;28(3):336-354. 
41. Marshall CA, Larkey LK, Curran MA, et al. Considerations of culture and social class for families 
facing cancer: the need for a new model for health promotion and psychosocial intervention. Fam Syst 
Health. 2011;29(2):81-94. 

 
 
 
 
Protocol Version Date:  3/3/2022 
 
 
Page 21 
 
42. Badger TA, Segrin C, Pasvogel A, Lopez AM. The effect of psychosocial interventions delivered by 
telephone vs. videophone to improve quality of life in early-stage breast cancer survivors and their 
supportive partners. . Journal of telemedicine and telecare. 2013;19(5):260-265. 
43. Badger TA, Segrin C, Hepworth JT, Pasvogel A, Weihs K, Lopez AM. Telephone-delivered health 
education and interpersonal counseling improve quality of life for Latinas with breast cancer and their 
supportive partners. Psycho-oncology. 2013;22(5):1035-1042. 
44. Foster C, Wright D, Hill H, Hopkinson J, Roffe L. Psychosocial implications of living 5 years or more 
following a cancer diagnosis: a systematic review of the research evidence. Eur J Cancer Care (Engl). 
2009;18(3):223-247. 
45. SEER Cancer Statistics Review, 1975-2008, National Cancer Institute. National Cancer Institute; 
2011. http://seer.cancer.gov/csr/1975_2008/. 
46. Salz T, Oeffinger KC, McCabe MS, Layne TM, Bach PB. Survivorship care plans in research and 
practice. CA: a cancer journal for clinicians. 2012. 
47. Hudson SV, Miller SM, Hemler J, et al. Adult cancer survivors discuss follow-up in primary care: 'not 
what i want, but maybe what i need'. Annals of family medicine. 2012;10(5):418-427. 
48. Taplin S, Rodgers, AB. Toward improving the quality of cancer care: Addressing the interfaces of 
primary and oncology-related subspecialty care. J Natl Cancer Inst 2010;40:3e10. 
49. Leach CR, Troeschel AN, Wiatrek D, Stanton AL, Diefenbach M, Stein KD, Sharpe K, Portier K. 
Preparedness and Cancer-Related Symptom Management among Cancer Survivors in the First Year 
Post-Treatment. Ann Behav Med. 2017. 
50. Cleeland CS, Fengmin Z, Chang VT, et al. The symptom burden of cancer: Evidence for a core set 
of cancer-related and treatment-related symptoms from the eastern cooperative oncology group 
symptom outcomes and practice patterns study. Cancer 2013. 
51. Mendoza TR Wang XS, Lu C, Palos GR, Liao, Z, Mobley GM, Kapoor S, Cleeland CS. Measuring 
the symptom burden of lung cancer the validity and utility of the lung cancer module of the M.D. 
Anderson Symptom Inventory. Oncologist. 2011;16(2):217-227. 
52. Stanton AL, Bernaards CA, Ganz PA. The BCPT symptom scales: a measure of physical symptoms 
for women diagnosed with or at risk for breast cancer. J Natl Cancer Inst. 2005;97(6):448-456. 
53. Howard-Anderson J, Ganz PA, Bower JE, Stanton AL. Quality of life, fertility concerns, and 
behavioral health outcomes in younger breast cancer survivors: a systematic review. J Natl Cancer 
Inst. 
2012;104(5):386-405. 
54. Ganz PA, Kwan L, Stanton AL, Bower JE, Belin TR. Physical and psychosocial recovery in the year 
after primary treatment of breast cancer. Journal of clinical oncology : official journal of the American 
Society of Clinical Oncology. 2011;29(9):1101-1109. 
55. Post KE, Flanagan J. Web based survivorship interventions for women with breast cancer: An 
integrative review. Eur J Oncol Nurs 2016; 25:90-99. 
56. Kang JH, Kwon JH, Hui D, Yennurajalingam S, Bruera E. Changes in Symptom Intensity Among 
Cancer Patients Receiving Outpatient Palliative Care. J Pain Symptom Manage. 2013. 
57. Given CW, Given B, Azzouz F, Kozachik S, Stommel M. Predictors of pain and fatigue in the year 
following diagnosis among elderly cancer patients. Journal of Pain and Symptom Management. 
2001;21(6):456-466. 
58. Dougherty P, Cata J, Cordella J, Burton A, Weng H. Taxol-induced sensory disturbance is 
characterized by preferential impairment of myelinated fiber function in cancer patients. Pain. 
2004;109:132-142. 
59. Dupont A, Bower JE, Stanton AL, Ganz PA. Cancer-Related Intrusive Thoughts Predict Behavioral 
Symptoms Following Breast Cancer Treatment. Health psychology : official journal of the Division of 
Health Psychology, American Psychological Association. 2013. 
60. Bower JE, Ganz PA, Irwin MR, Kwan L, Breen EC, Cole SW. Inflammation and behavioral 
symptoms after breast cancer treatment: Do fatigue, depression and sleep disturbance share a 
common 
underlying mechanism? . Journal of Clinical Oncology. 2011;29(26):3517-3522. 
61. Owen JE, O'Carroll Bantum E, Pagano IS, Stanton A. Randomized Trial of a Social Networking 
Intervention for Cancer-Related Distress. Ann Behav Medicine 2017. 

 
 
 
 
Protocol Version Date:  3/3/2022 
 
 
Page 22 
 
62. Schrepf A, Clevenger L, Christensen D, et al. Cortisol and inflammatory processes in ovarian 
cancer 
patients following primary treatment: relationships with depression, fatigue, and disability. Brain, 
behavior, and immunity. 2013;30 Suppl:S126-134. 
63. Clevenger L, Schrepf A, Christensen D, et al. Sleep disturbance, cytokines, and fatigue in women 
with ovarian cancer. Brain, behavior, and immunity. 2012;26(7):1037-1044. 
64. Jacobsen PB, Donovan KA, Swaine ZN, Watson IS. Management of anxiety and depression in 
adult 
cancer patients:Toward an evidence-based approach. In: Chang AE, Ganz DF, Halyes DF, eds. 
Oncology : an evidence-based approach. New York: Springer-Verlag; 2006:1552-1579. 
65. Butt Z, Rosenbloom SK, Abernethy AP, et al. Fatigue is the most important symptom for advanced 
cancer patients who have had chemotherapy. Journal of the National Comprehensive Cancer Network 
(JNCCN). 2008;6(5):448-455. 
66. Mock V. Evidence-based treatment for cancer-related fatigue. Journal of the National Cancer 
InstituteMonographs. 2004;(32)(32):112-118. 
67. Given B. Cancer-related fatigue: a brief overview of current nursing perspectives and experiences. 
Clinical Journal of Oncology Nursing. 2008;12(5 Suppl):7-9. 
68. Miaskowski C, Barsevick A, Berger A, Casagrande R, Grady PA, Jacobsen P, Kutner J, Patrick D, 
Zimmerman L, Xiao C, Matocha M, Marden S. Advancing Symptom Science Through Symptom Cluster 
Research: Expert Panel Proceedings and Recommendations. J Natl Cancer Inst 2017;109(109): 
pii:djw253. 
69. Miaskowski C, Dunn L, Ritchie C, Paul SM, Cooper B, Aouizerat BE, Alexander K, Skerman H, 
Yates P. Latent Class Analysis Reveals Distinct Subgroups of Patients Based on Symptom Occurrence 
and Demographic and Clinical Characteristics. J Pain Symptom Manage. 2015;50(1):28-37. 
70. Barsevick, A. Defining the Symptom Cluster: How Far Have We Come? Semin Oncol Nurs 
2016;32(4):334-350. 
71. Kwekkeboom, KL. Cancer Symptom Cluster Management. Semin Oncol Nurs. 2016;32(4):373-384. 
72. Bower JE, Garet D, Sternlieb B, et al. Yoga for persistent fatigue in breast cancer survivors: a 
randomized controlled trial. Cancer. 2012;118(15):3766-3775. 
73. Maly RC, Liang LJ, Liu Y, Griggs JJ, Ganz PA. Randomized Controlled Trial of Survivorship Care 
Plans Among Low-Income, Predominantly Latina Breast Cancer Survivors. Journal of clinical oncology : 
official journal of the American Society of Clinical Oncology. 2017:JCO2016689497. 
74. Mustian KM Alfano CM, Heckler C, Kleckner AS, Kleckner IR, Leach CR, Mohr D, Palesh OG, 
Peppone LJ, Piper BF, Scarpato J, Smith T, Sprod LK, Miller SM. Comparison of Pharmaceutical, 
Psychological, and Exercise Treatments for Cancer-Related Fatigue: A Meta-analysis. JAMA Oncology. 
2017. 
75. Van Den Berg SW Gielissen MFM, Custers JAE, Van Der Graaf WTA, Ottevanger PB, Prins JB 
BREATH: web-based self-management for psychological adjustment after primary breast cancer 
results 
of a multicenter randomized controlled trial Journal of Clinical Oncology. 2015;33(25):2766e2771. 
76. Ritchie C, Dunn LB, Paul SM, et al. Differences in the symptom experience of older oncology 
outpatients. J Pain Symptom Manage. 2014;47(4):697-709. 
77. Sohl SJ Dietrich MS, Wallston KA, Ridner SH. A randomized controlled trial of expressive writing in 
breast cancer survivors with lymphedema. Psychol Health. 2017:1-17. 
78. Bayliss EA, Ellis JL, Steiner JF. Barriers to self-management and quality-of-life outcomes in seniors 
with multimorbidities Annals of family medicine. 2007;5(5):395-402. 
79. Bender CM, Engberg SJ, Donovan HS, et al. Symptom clusters in adults with chronic health 
problems and cancer as a comorbidity. Oncology Nursing Forum. 2008;35(1): E-11. 
80. Das SC, Khurana H, Gupta D, Mishra S, Bhatnagar S. Comorbidities in a cancer patient: problems 
in 
pain management and palliation. Am J Hosp Palliat Care. 2009;26(1):60-63. 
81. Gerbershagen HJ, Ozgur E, Straub K, et al. Prevalence, severity, and chronicity of pain and general 
health-related quality of life in patients with localized prostate cancer. European journal of pain 
(London, England). 2008;12(3):339-350. 

 
 
 
 
Protocol Version Date:  3/3/2022 
 
 
Page 23 
 
82. Harlan LC, Klabunde CN, Ambs AH, et al. Comorbidities, therapy, and newly diagnosed conditions 
for women with early-stage breast cancer. J Cancer Surviv. 2009;3(2):89-98. 
83. Harris EE, Hwang WT, Urtishak SL, Plastaras J, Kinosian B, Solin LJ. The impact of comorbidities 
on outcomes for elderly women treated with breast-conservation treatment for early-stage breast 
cancer. 
Int J Radiat Oncol Biol Phys. 2008;70(5):1453-1459. 
84. Pinkawa M, Fischedick K, Gagel B, et al. Impact of age and comorbidities on health-related quality 
of life for patients with prostate cancer: evaluation before a curative treatment. BMC Cancer. 2009; 
9:296. 
85. Satariano WA, Muss HB. Effects of comorbidity on cancer. US National Institutes of Health, 
National Institute on Aging conference, August; 2009. 
86. Cleeland CS. Symptom burden: Multiple symptoms and their impact as patient-reported outcomes. 
Journal of the National Cancer Institute Monographs. 2007; 37:16-21. 
87. Ahern TP, Lash TL, Thwin SS, Silliman RA. Impact of acquired comorbidities on all-cause mortality 
rates among older breast cancer survivors. Medical care. 2009;47(1):73-79. 
88. Aschele C, Bergamo F, Lonardi S. Chemotherapy for operable and advanced colorectal cancer. 
Cancer treatment reviews. 2009;35(6):509-516. 
89. Kelly D. Delays in the diagnosis of lung cancer occurred because patients failed to recognise 
symptoms as serious and warranting medical attention. Evidence-based nursing. 2006;9(4):127. 
90. Miller AH, Ancoli-Israel S, Bower JE, Capuron L, Irwin MR. Neuroendocrine-immune mechanisms 
of behavioral comorbidities in patients with cancer. Journal of clinical oncology : official journal of the 
American Society of Clinical Oncology. 2008;26(6):971-982. 
91. Mukerji SS, Duffy SA, Fowler KE, Khan M, Ronis DL, Terrell JE. Comorbidities in head and neck 
cancer: agreement between self-report and chart review. Otolaryngology--head and neck surgery : 
official journal of American Academy of Otolaryngology-Head and Neck Surgery. 2007;136(4):536-542. 
92. Ozanne EM, Braithwaite D, Sepucha K, Moore D, Esserman L, Belkora J. Sensitivity to input 
variability of the Adjuvant! Online breast cancer prognostic model. Journal of clinical oncology : official 
journal of 
the American Society of Clinical Oncology. 2009;27(2):214-219. 
93. Perkins HY, Baum GP, Taylor CL, Basen-Engquist KM. Effects of treatment factors, comorbidities 
and health-related quality of life on self-efficacy for physical activity in cancer survivors. Psycho-
oncology.2009;18(4):405-411. 
94. Siegelmann-Danieli N, Khandelwal V, Wood GC, et al. Breast cancer in elderly women: outcome as 
affected by age, tumor features, comorbidities, and treatment approach. Clin Breast Cancer. 
2006;7(1):59-66. 
95. Chochinov HM. Depression in cancer patients. The Lancet Journal. 2001;2:499-506. 
96. Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. 
Applied Psychological Measurement. 1977;1(3):385-401. 
97. Sikorskii A, Given C, You M, Jeon S, Given B. Response analysis for multiple symptoms revealed 
differences between arms of a symptom management trial. Journal of clinical epidemiology. 
2009;62(7):716-724. 
98. Lee L, Cheung WY, Atkinson E, Krzyzanowska MK. Impact of comorbidity on chemotherapy use 
and outcomes in solid tumors: a systematic review. Journal of clinical oncology : official journal of the 
American Society of Clinical Oncology. 2011;29(1):106-117. 
99. Geiss LS, Wang J, Cheng YJ, et al. Prevalence and incidence trends for diagnosed diabetes 
among 
adults aged 20 to 79 years, United States, 1980-2012. JAMA. 2014;312(12):1218-1226. 
100. Daviglus ML, Talavera GA, Aviles-Santa ML, et al. Prevalence of major cardiovascular risk factors 
and cardiovascular diseases among Hispanic/Latino individuals of diverse backgrounds in the United 
States. JAMA. 2012;308(17):1775-1784. 
101. Ashing-Giwa KT, Tejero JS, Kim J, Padilla GV, Hellemann G. Examining predictive models of 
HRQOL in a population-based, multiethnic sample of women with breast carcinoma. Quality of Life 
Research. 2007;16(3):413-428. 

 
 
 
 
Protocol Version Date:  3/3/2022 
 
 
Page 24 
 
102. Ashing-Giwa KT, Padilla G, Tejero J, et al. Understanding the breast cancer experience of women: 
a qualitative study of African American, Asian American, Latina and Caucasian cancer survivors. 
Psychooncology. 2004;13(6):408-428. 
103. Eversley R, Estrin D, Dibble S, Wardlaw L, Pedrosa M, Favila-Penney W. Post-treatment 
symptoms among ethnic minority breast cancer survivors. Oncol Nurs Forum. 2005;32(2):250-256. 
104. Guy GP, Yabroff R, Ekweme DU, Rim SH, Li R, Richardson LC. Exonomic burden of chronic 
conditions among survivors of cancer in the United States. Journal of Clinical Oncology. 2017;35:1-9. 
105. Khatcheressian JL, Hurley P, Bantug E, Esserman LJ, Grunfeld E, Halberg F, Hantel A, Henry NL, 
Muss HB, Smith TJ, Vogel VG, Wolff AC, Somerfield MR, Davidson NE Breast cancer follow-up and 
management after primary treatment: American Society of Clinical Oncology clinical practice guideline 
update. Journal of Clinical Oncology, 31(7): 961-965. . Journal of Clinical Oncology. 2013;31(7):961- 
965. 
106. Meyerhardt JA, Mangu PB, Flynn PJ, Korde L, Loprinzi CL, Minsky BD, Petrelli NJ, Ryan K, 
Schrag DH, Wong SL, Benson AB 3rd. . Follow-up care, surveillance protocol, and secondary 
prevention measures for survivors of colorectal cancer: American Society of Clinical Oncology clinical 
practice guideline endorsement. Journal of Clinical Oncology 2013;31(35):4465-4470. 
107. Mooney K, Beck S, Friedman R, Ramesh F. Telephone-linked care for cancer symptom 
monitoring. Cancer Practice. 2002;10(3):147-154. 
108. Klerman GL, Weissman, MM. New applications of interpersonal psychotherapy. Washington, DC: 
American Psychological Association; 1993. 
109. Markowitz J.C. & Weissman MM. Interpersonal psychotherapy: Past, present and future. Clinical 
Psychology and Psychotherapy. 2012;19:99-105. 
110. Weissman MM, Markowitz JC. An overview of internpersonal psychotherapy. In: Markowitz JC, ed. 
Interpersonal psychotherapy. Washington,DC: American Psychiatric Press; 1998:1-33. 
Contact PD/PI: BADGER, TERRY A.  
111. Weissman MM, Markowitz JC, Klerman GL. Comprehensive guide to interpersonal psychotherapy. 
New York: Basic Books; 2000. 
112. Cuijpers P, Donker T, Weissman MM, Ravitz P, Cristea IA. Interpersonal Psychotherapy for 
Mental Health Problems: A Comprehensive Meta-Analysis. American Journal of Psychiatry. 2016. 
113. Badger T, Segrin C, Pasvogel A, Lopez AM. The effect of psychosocial interventions delivered by 
telephone and videophone on quality of life in early-stage breast cancer survivors and their supportive 
partners. Journal of telemedicine and telecare. 2013;19(5):260-265. 
114. Schuz N, Walters JA, Cameron-Tucker H, Scott J, Wood-Baker R, Walters EH. Patient Anxiety 
and Depression Moderate the Effects of Increased Self-management Knowledge on Physical Activity: A 
Secondary Analysis of a Randomised Controlled Trial on Health-Mentoring in COPD. COPD. 
2015;12(5):502-509. 
115. Wagner GJ, Ghosh-Dastidar B, Robinson E, et al. Effects of Depression Alleviation on ART 
Adherence and HIV Clinic Attendance in Uganda, and the Mediating Roles of Self-Efficacy and 
Motivation. AIDS Behav. 2016. 
116. Nahum-Shani I, Qian M, Almirall D, et al. Q-learning: A data analysis method for constructing 
adaptive interventions. Psychological methods. 2012;17(4):478. 
117. Nahum-Shani I, Qian M, Almirall D, et al. Experimental design and primary data analysis methods 
for comparing adaptive interventions. Psychological methods. 2012;17(4):457. 
118. Report BR. Moonshot Cancer Initiative. 2016. 
119. Chakraborty B, Murphy S. Inference for non-regular parameters in optimal dynamic treatment 
regimes. Statistical Methods in Medical Research. 2010;19(3):317-343. 
120. Collins L, Murphy S, Bierman K. A conceptual framework for adaptive preventive interventions. 
Prevention Science. 2004;5(3):185-196. 
121. Rush AJ, Fava M, Wisniewski SR, et al. Sequenced treatment alternatives to relieve depression 
(STAR*D): Rationale and design. Controlled Clinical Trials. 2003;25(1):119-142. 
122. Schneider LS, Tariot PN, Lyketsos CG, et al. National Institute of Mental Health clinical 
antipsychotic trials of intervention effectiveness (CATIE): Alzheimer disease trial methodology. 
American Journal of Geriatric Psychiatry. 2001;9(4):346-360. 
123. Cashion AK, Grady, PA. The National Institutes of Health/National Institutes of Nursing Research 

 
 
 
 
Protocol Version Date:  3/3/2022 
 
 
Page 25 
 
intramural research program and the development of the National Institutes of health Symptom Science 
Model. Nursing Outlook. 2015;63(4):484-487. 
124. Family Caregiver Alliance. Caregiver Assessment: Principles, Guidelines and Strategies for 
Change. San Francisco: Report from a National Consensus Development Conference (Vol. I);2006. 
125. Ownsworth T, Henderson L, Chambers SK. Social support buffers the impact of functional 
impairments on caregiver psychological well-being in the context of brain tumor and other cancers. 
Psychooncology. 2010;19(10):1116-1122. 
126. Milbury K, Badr H, Fossella F, Pisters KM, Carmack CL. Longitudinal associations between 
caregiver burden and patient and spouse distress in couples coping with lung cancer. Support Care 
Cancer. 2013;21(9):2371-2379. 
127. Choi YS, Hwang SW, Hwang IC, et al. Factors associated with quality of life among family 
caregivers of terminally ill cancer patients. Psycho-oncology. 2016;25(2):217-224. 
128. Butow PN, Price MA, Bell ML, et al. Caring for women with ovarian cancer in the last year of life: a 
longitudinal study of caregiver quality of life, distress and unmet needs. Gynecol Oncol. 
2014;132(3):690-697. 
129. Badger TA, Segrin C, Figueredo AJ, et al. Psychosocial interventions to improve quality of life in 
prostate cancer survivors and their intimate or family partners. Quality of life research : an international 
journal of quality of life aspects of treatment, care and rehabilitation. 2011;20(6):833-844. 
130. Gaugler JE, Linder J, Given CW, Kataria R, Tucker G, Regine WF. Family cancer caregiving and 
negative outcomes: the direct and mediational effects of psychosocial resources. Journal of family 
nursing. 2009;15(4):417-444. 
131. Badger TA, SegrinC, Dorros SM, Meek P, Lopez AM . Depression and anxiety in women with 
breast cancer and their partners. Nursing Research. 2007;56:44-53. 
132. American Cancer Society A. Cancer Facts & Figures for Hispanics/Latinos 2015-2017. Atlanta, 
GA: American Cancer Society; 2015. 
133. Cooley ME, Short TH, Moriarty HJ. Symptom prevalence, distress, and change over time in adults 
receiving treatment for lung cancer. Psycho-oncology. 2003;12(7):694-708. 
134. Kroenke K. Studying symptoms: sampling and measurement issues. Annals of internal medicine. 
2001;134(9 Pt 2):844-853. 
135. Sikorskii A, Wyatt G. Using SMART Design to Improve Symptom Management Strategies among 
Cancer Patients. NCI; 2015-2019. 
136. Im E-O. To enhance breast cancer survivorship of Asian Americans. NCI; 2016-2021. 
137. Sikorskii A, Given C, You M, Jeon S, Given BA. Response analysis for multiple symptoms 
revealed differences between arms of a symptom management trial. Journal of Clinical Epidemiology. 
2009;62(7):716-724. 
138. Sikorskii A, Given C, Given B, Jeon S, Decker V, Decker D, Champion V, McCorkle R. Symptom 
management for cancer patients: a trial comparing two multimodal interventions. J Pain Symptom 
Management 2007;34(3):253-264. 
139. Given CW, Sikorskii A, Tamkus D, Given B, You M, McCorkle R, Champion V, Decker D. 
Managing symptoms among patients with breast cancer during chemotherapy: results of a two-arm 
behavioral trial. Journal of Clinical Oncology. 2008;26(36):5855-5862. 
140. Badger T, Segrin C, Meek P, Lopez AM, Bonham E, Sieger A. Telephone interpersonal counseling 
with women with breast cancer: symptom management and quality of life. Oncology Nursing Forum. 
2005;32(2):273-279. 
141. Badger TA, Segrin C, Dorros SM, Meek P, Lopez AM . Depression and anxiety in women with 
breast cancer and their partners. Nursing Research. 2007(56):44-53. 
142. Rehse B, Pukrop R. Effects of psychosocial interventions on quality of life in adult cancer patients: 
meta analysis of 37 published controlled outcome studies. Patient Education and Counseling. 
2003;50(2):179-186. 
143. Segrin C, Badger T, Pasvogel A. Loneliness and emotional support predict physical and 
psychological distress in Latinas with breast cancer and their supportive partners. The Open 
Psychology Journal. 2015;8(1):105-112. 

 
 
 
 
Protocol Version Date:  3/3/2022 
 
 
Page 26 
 
144. Given C, Given B, Rahbar M, et al. Effect of a cognitive behavioral intervention on reducing 
symptom severity during chemotherapy. Journal of Clinical Oncology 2004;22(3):507-516. 
145. Given C, Given B, Bradley CJ, Krauss JC, Sikorskii A, Vachon E. Dynamic Assessment of Value 
During High-Cost Cancer Treatment: A Response to American Society of Clinical Oncology and 
European Society of Medical Oncology. Journal of Oncology Practice. 2016. 
146. Pew. Pew Internet & American Life Project: Mobile Access 2010. 2010; 
pewinternet.org/Reports/2010/Mobile-access-2010. Accessed May 15, 2012. 
147. Ruskin PE, Silver-Aylaian M, Kling MA, et al. Treatment outcomes in depression: comparison of 
remote treatment through telepsychiatry to in-person treatment. The American Journal of Psychiatry. 
2004;161(8):1471-1476. 
148. Kornblith AB, Dowell JM, Herndon JE 2nd, Engelman BJ, Bauer-Wu S, Small EJ, Morrison VA, 
Atkins J, Cohen HJ, Holland JC. Telephone monitoring of distress in patients aged 65 years or older 
with advanced stage cancer. Cancer. 2006;107(11):2706-2714. 
149. Sikorskii A, Given C, Given B, Jeon S, You M. Differential symptom reporting by mode of 
administration of the assessment: automated voice response system versus a live telephone interview. 
Medical Care Care. 2009;47(8):866-874. 
150. Segrin C, Badger T. Psychological distress in different social network members of breast and 
prostate cancer patients. Research in nursing & health. 2010;33(5):450-464,. 
151. Segrin C, Badger T, Lopez AM. The effect of dosage on women and their partners' response to 
telephone counseling. Unpublished manuscript. 2009(Journal Article). 
152. Badger T, Segrin C, Figueredo AJ, et al. Psychosocial interventions to improve quality of life in 
prostate cancer survivors and their intimate partners. Quality of Life Research. 2011;20:833-844. 
153. Badger T, Segrin C, Meek P, Lopez AM, Bonham E. A case study of telephone interpersonal 
counseling for women with breast cancer and their partners. Oncology Nursing Forum. 2004;31(5):997- 
1003. 
154. Badger TA, Segrin C, Meek P, Lopez AM, Bonham E. Profiles of women with breast cancer: Who 
responds to a telephone interpersonal counseling intervention? Journal of psychosocial oncology. 
2005;23(2-3):79-100. 
155. Given B GC, Sikorskii A, You M, McCorkle R, Champion V. Analyzing symptom management 
trials: the value of both intention-to-treat and per-protocol approaches. Oncol Nurs Forum 2009;36(6): 
E293-302. 
156. Given BA, Given C, Jeon S, Sikorskii A. Effect of neutropenia on the impact of a cognitive-
behavioral intervention for symptom management. Cancer. 2005;104(4). 
157. Given C, Bradley CJ, You M, Sikorskii A. Costs of novel symptom management interventions and 
their impact on hospitalizations. Journal of Pain & Symptom Management. 2010;39(4):663-672. 
158. Crane TE, Badger, Terry A, Hsu, CH, Segrin, C, Rosenfeld, AG. Occurrence of Symptom Clusters 
in Latina Breast Cancer Survivors. Tucson, Arizona: The University of Arizona; 2016. 
159. Crane TE, Badger T, Hsu CH, Segrin C, Rosenfeld AG. Trajectories of Anxiety and Depression in 
Latina Breast Cancer Survivors. Tucson, Az: The University of Arizona; 2016. 
160. Thomson CA, Crane TE, Miller A, Garcia DO, Basen-Engquist K, Alberts DS. A randomized trial of 
diet and physical activity in women treated for stage II-IV ovarian cancer: Rationale and design of the 
Lifestyle Intervention for Ovarian Cancer Enhanced Survival (LIVES): An NRG Oncology/Gynecologic 
Oncology Group (GOG-225) Study. Contemp Clin Trials. 2016; 49:181-189. 
161. George SM, Ballard R, Shikany JM, Crane TE, Neuhouser ML. A prospective analysis of diet 
quality and endometrial cancer among 84,415 postmenopausal women in the Women's Health 
Initiative. 
Annals of epidemiology. 2015;25(10):788-793. 
162. Thomson CA, Crane TE, Wertheim BC, et al. Diet quality and survival after ovarian cancer: results 
from the Women’s Health Initiative. Journal of the National Cancer Institute. 2014;106(11):dju314. 
163. Butalla AC, Crane TE, Patil B, Wertheim BC, Thompson P, Thomson CA. Effects of a carrot juice 
intervention on plasma carotenoids, oxidative stress, and inflammation in overweight breast cancer 
survivors. Nutrition and cancer. 2012;64(2):331-341. 
164. Crane TE, Kubota C, West JL, Kroggel MA, Wertheim BC, Thomson CA. Increasing the vegetable 
intake dose is associated with a rise in plasma carotenoids without modifying oxidative stress or 

 
 
 
 
Protocol Version Date:  3/3/2022 
 
 
Page 27 
 
inflammation in overweight or obese postmenopausal women. The Journal of nutrition. 
2011;141(10):1827-1833. 
165. Huynh L, Johns B, Liu SH, Vedula SS, Li T, Puhan MA. Cost-effectiveness of health research 
study participant recruitment strategies: a systematic review. Clin Trials. 2014;11(5):576-583. 
166. Homish GG, Leonard KE. Testing methodologies to recruit adult drug-using couples. Addictive 
Behaviors. 2009;34(1):96-99. 
167. Boult C, Boult LB, Morishita L, Dowd B, Kane RL, Urdangarin CF. A randomized clinical trial of 
outpatient geriatric evaluation and management. Journal of the American Geriatrics Society. 
2001;49(4):351-359. 
168. Yancey AK, Ortega AN, Kumanyika SK. Effective recruitment and retention of minority research 
participants. Annual Review of Public Health. 2006;27(Journal Article):1-28. 
169. Roosa MW, Liu, F.F., Torrez, M., Gonzalez, N.A., Knight, G.P., Saenz, D. Sampling and 
recruitment in studies of cultural influences on adjustment: A case study with Mexican-Americans. 
Journal of Family Psychology. 2008;22:293-302. 
170. London L, Hurtado-de-Mendoza, A, Song, M, Nagriimadugu, A, Luta, G, Sheppard, VB. Motivators 
and barriers to Latinas' participation in clinical trials: the role of contextual factors. Contemporary 
Clinical Trials. 2015;40:74-80. 
171. Ashing-Giwa KT. Can a culturally responsive model for research desing bring us closer to 
addressing participation disparities? Lessons learned from cancer survivorship studies. Ethnicity & 
disease. 2005;15:130-137. 
172. Hawkins RP, Kreuter M, Resnicow K, Fishbein M, Dijkstra A. Understanding tailoring in 
communicating about health. Health Education Research. 2008;23(3):454-466. 
173. Resnicow K, Baranowski T, Ahluwalia JS, Braithwaite RL. Cultural sensitivity in public health: 
defined and demystified. Ethnicity & Disease. 1999;9(1):10-21. 
174. Badger TA, Segrin C, Swiakowski P, McNelis M, Weihs K, Lopez AM. Why Latinas select Specific 
Informal Caregivers to Participate with Them in Psychosocial Interventions. Journal of Transcultural 
Nursing. 2016:1-7. 
175. Bayliss EA, Ellis JL, Steiner JF. Seniors’ self-reported multimorbidity captured biopsychosocial 
factors not incorporated into two other data-based morbidity measures. Journal of clinical epidemiology. 
2009;62(5):550-557. 
176. Taves DR. Minimization: a new method of assigning patients to treatment and control groups. 
Clinical pharmacology and therapeutics. 1974;15(5):443-453. 
177. Therneau TM. How many stratification factors are too many to use in a randomization plan? 
Controlled Clinical Trials. 1993;14:98-108. 
178. Farrar JT, Portenoy RK, Berlin JA, Kinman JL, Strom BL. Defining the clinically important 
difference in pain outcome measures. Pain. 2000;88(3):287-294. 
179. Miaskowski C, Dodd M, West C, et al. The use of a responder analysis to identify differences in 
patient outcomes following a self-care intervention to improve cancer pain management. Pain. 
2007;129(1- 2):55-63. 
180. Jones PS, Lee, J.W., Phillips, L.R., Zhang, X.E.. & Jaciedo, K.B. . An adaptation of Brislin’s 
translation model for cross cultural research. Nursing Research. 2001;50 (5):300-304. . 
181. Morgan MA. Cancer survivorship: history, quality-of-life issues, and the evolving multidisciplinary 
approach to implementation of cancer survivorship care plans. Oncology Nursing Forum. 
2009;36(4):429-436. 
182. Ell K, Xie, B., Kapetanovic, S. et al. One-year follow-up of collaborative depression care for low-
income predominately Hispanic patients with cancer. Psychiatric services (Washington, DC). 
2011;62(2):162- 170. 
183. Bellg AJ, Borrelli B, Resnick B, et al. Enhancing treatment fidelity in health behavior change 
studies: Best practices and recommendations from the NIH Behavior Change Consortium. Health 
psychology: official journal of the Division of Health Psychology, American Psychological Association. 
2004;23(5):443-451. 
184. Badger TA. A nursing perspective of depression in cancer survivors and their partners. Nursing 
Science Quarterly. 2008;21(1):43-44. 

 
 
 
 
Protocol Version Date:  3/3/2022 
 
 
Page 28 
 
185. Segrin C, Badger TA. Psychological and physical distress are interdependent in breast cancer 
survivors and their partners. Psychology, health & medicine. 2014;19(6):716-723. 
186. Segrin C, Badger T, Meek P, Lopez AM, Bonham E, Sieger A. Dyadic interdependence on affect 
and quality-of-life trajectories among women with breast cancer and their partners. Journal of Social 
and Personal Relationships. 2005;22(5):673-689. 
187. Greeno CG, Anderson CM, Shear MK, Mike G. Initial treatment engagement in a rural community 
mental health center. Psychiatric Services. 1999;50(12):1634-1636. 
188. Segrin C, Badger TA. Psychological distress in different social network members of breast and 
prostate cancer survivors. Res Nurs Health. 2010;33(5):450-464. 
189. Correia H. Spanish translations of PROMIS Instruments. In: Segrin C, ed. Northwestern 
University: Dept. of Medical Social Sciences; 2011. 
190. Cella D, Choi S, Garcia S, et al. Setting standards for severity of common symptoms in oncology 
using the PROMIS item banks and expert judgment. Qual Life Res 2014; 12/23(10):2651-2661. 
191. Badger TA, Segrin C, Meek P. Development and validation of an instrument for rapidly assessing 
symptoms: the general symptom distress scale. J Pain Symptom Manage. 2011;41(3):535-548. 
192. Given B, Given CW, McCorkle M, et al. Pain and fatigue management: results of a nursing 
randomized clinical trial. Oncology Nursing Forum. 2002;29(6):949-956. 
193. Lewis FM, Fletcher KA, Cochrane BB, Fann JR. Predictors of depressed mood in spouses of 
women with breast cancer. Journal of Clinical Oncology. 2008;26(8):1289-1295. 
194. Cella D, Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S. et al. The Patient-Reported 
Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult 
self-reported health outcome item banks: 2005-2008. Journal of clinical epidemiology. 2010;63:1179-
1194. 
195. Cella D, Riley W, Stone A, et al. Initial item banks and first wave testing of the Patient-Reported 
Outcomes Measurement Information System (PROMIS) network: 2005-2008. Journal of clinical 
epidemiology. 2010;63:1179-1194. 
196. PROMIS. Adult Depression version 1.0 short form: a breif guide to the 8-item PROMIS Short Form 
v1.0-Depression 8b. 2012; http://assessmentcenter.net. 
197. PROMIS. Adult satisfaction with participation in social roles profile short forms. 2013; 
Http://assessmentcenter.net. 
198. PROMIS. Adult physical function profile short forms. 2013; http://assessmentcenter.net. 
199. Little R, Rubin M. Statistical Analysis with Missing Data. New York: Wiley and Sons; 1987. 
200. Hogan JW, Roy J, Korkontzelou C. Handling drop-out in longitudinal studies. Statistics in 
Medicine. 
2004;23(9):1455-1497. 
201. Shen C, Weissfeld L. Application of pattern-mixture models to outcomes that are potentially 
missing not at random using pseudo maximum likelihood estimation. Biostatistics. 2005;6(2):333-347. 
202. SAS Software. Version 9 of the SAS System for Windows. Cary, NC: Copyright 2002-2003 SAS 
Institute Inc. SAS and all other SAS Institute Inc. product or service names are registered trademarks 
or trademarks of SAS Institute Inc.; 2002-2003. 
203. Lee E, Wei L, Amato D. Cox-Type regression analysis for large numbers of small groups of 
correlated failure time observations. Netherlands: Kluwer Academic Publishers; 1992. 
204. Lin D. Cox regression analysis of multivariate failure time data: The marginal apprach. Statistics in 
Medicine. 1994;13:2233-2247. 
205. Preacher KJ, Selig JP. Advantages of Monte Carlo confidence intervals for indirect effects. 
Communication Methods and Measures. 2012;6:77-98. 
206. Biesanz JC, Falk CF, Savalei V. Assessing mediational models: Testing and interval estimation for 
indirect effects. . Multivariate Behavioral Research. 2010;45:661-701. 
207. Given CW, Given BA. Symptom Management and Psychosocial Outcomes Following Cancer In: 
Oeffinger K, ed. Submitted to Seminars in Oncology, Special Edition 2013. 
208. Robbins JM. Optimal structured nested models for the optimal sequential decisions. In: Lin DY, 
Heagerty PJ, eds. Proceedings of the Second Seattle Wymposium on Biostatistics. New York, NY: 
Springer; 2004. 
209. Moodie EE, Richardson TS, Stephens DA. Demystifying optimal dynamic treatment regimes. 

 
 
 
 
Protocol Version Date:  3/3/2022 
 
 
Page 29 
 
Biometrics. 2007;63(2):447-455. 
210. PROC QLEARN (Version 1.0) 2012; http://methodology.psu.edu/downloads/procqlearn. Accessed 
May 5, 2013. 
211. Murphy SA. Optimal dynamic treatment regimes (with discussions). Journal of the Royal Statistical 
Society, Series B. 2003;65:331-366. 
212. Murphy SA. A Generalization Error for Q-Learning. Journal of machine learning research : JMLR. 
2005;6:1073-1097. 
213. Sutton RS, Barto AG. Reinforcement learning: An introduction. Cambridge, MA: MIT Press; 1998. 
214. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach 
to multiple testing. J of the Royal Statistical Society, Series B. 1995;57(1):289-300. 
215. Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under 
dependency. Annals of Statistics. 2001;29(4):1165-1188. 
216. Thissen D, Steinberg L, Kuang D. Quick and easy implementation of the Benjamini-Hochberg 
procedure for controlling the false positive rate in multiple comparisons. Journal of Educational and 
Behavioral Statistics. 2002;27(1):77-83. 
217. Swanson GM, Ward AJ. Recruiting minorities into clinical trials toward a participant-friendly 
system. Journal of the National Cancer Institute. 1995;87(23):1747-1759. 
218. Lindenstruth KA, Curtis CB, Allen JK. Recruitment of African-American and White postmenopausal 
women into clinical trials: The beneficial effects of soy trial experience. Ethn Dis. 2006;16(4):938-942. 
219. Paskett ED, Katz ML, DeGraffinreid CR, Tatum CM. Participation in cancer trials: Recruitment of 
underserved populations. Clinical Advances in Hematology & Oncology. 2003;1(10):607-613. 
 
14.0 
APPENDICES 
 
APPENDIX I – FORMS 
 
• 
Recruitment Script 
• 
Contact Form (internal document) 
• 
Telephone survey (baseline and 13-week follow up) 
• 
Debriefing and satisfaction  
• 
Medical Record Audit Form (internal document) 
 
APPENDIX II – STUDY INFORMATION 
 
• 
Study Brochure 
• 
Study Flyer 
• 
Study webpage 
  
 
APPENDIX III – INTERVENTION INFORMATION 
 
• 
Symptom Management and Survivorship Handbook  
• 
Weekly GSDS script 
• 
Handbook form (internal document) 
• 
TIP-C form (internal document)