Improving Participation in Cardiac Rehabilitation Among Lower-Socioeconomic Status Patients: Efficacy of Early Case Management and Financial Incentives
Phase: PHASE2
Conditions: Cardiac Rehabilitation
Sponsor: University of Vermont

HS Protocol Form 7/14/17
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HS Protocol Form 7/14/17
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CHRMS (M di
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CHRMS (M di
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CHRMS (M di
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HS Protocol Form 7/14/17
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HS Protocol Form 7/14/17
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HS Protocol Form 7/14/17
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CHRMS (Medical) #18 0516 Approved: 2/8/2022
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CHRMS (M di
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HS Protocol Form 7/14/17
FINANCIAL CONSIDERATIONS 
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CHRMS (Medical) #18-0516 Approved: 2/8/2022
FINANCIAL CONSIDERATIONS
FINANCIAL CONSIDERATIONS
di
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d 2/8/2022

HS Protocol Form 7/14/17
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CHRMS (Medical) #18-0516 Approved: 2/8/2022
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Statistical Analysis Plan 
Sample Size and Power 
Sample sizes were calculated to provide sufficient power to calculate differences in our primary outcome (CR 
adherence, as measured by % completing 36 sessions) between the control condition and any of the three 
intervention conditions as well as between the combined intervention and either of the interventions delivered 
alone. Estimates were based on a series of studies conducted within our research group.  
Of most relevance, preliminary data from our prior trial on the use of incentives to increase CR rates among 
lower-SES individuals showed 54% of those receiving incentives completed the 36 sessions compared to 28% 
of those receiving usual care. Using these proportions and the likelihood ratio test for two proportions in SAS 
POWER, 56 individuals per condition would be needed to detect a significant difference between conditions 
with 80% power. 
A seminal set of studies, conducted by a member of our research team (Co-I Higgins), that systematically 
determined the efficacy of case management and incentives, alone and in combination, provided additional 
data for the power estimates. This set of studies, conducted in the 1990’s and early 2000’s compared 
treatment conditions in programs for achieving cocaine abstinence among individuals with cocaine 
dependency.54-58 Although the primary outcome variable of these studies – cocaine abstinence, either 
continuously or at selected time points – differs from this study’s focus on CR attendance, they are similar in 
terms of providing case management services, with or without incentives, to demographically comparable 
individuals to boost their EF, improving adherence to a program attempting to change behavior, and thereby 
instill long-term health benefits. Two of these earlier studies56,57 compared the effects of a community 
reinforcement approach (i.e., case management) with incentives to usual care in achieving continuous cocaine 
abstinence at the end of four to 16 weeks of treatment. The third study54 tested the effects of the addition of 
incentives vs, usual care. Using condition proportions who achieved continuous cocaine abstinence at these 
time periods and the likelihood ratio test for two proportions in SAS POWER, we calculate sample size of each 
treatment condition vs. usual care required a maximum of 25 participants to achieve 80% power.  
Subsequent studies examined initial cocaine abstinence among individuals with cocaine dependence 
comparing case management with and without incentives, case management with contingent incentives versus 
case management with non-contingent incentives, and case management with incentives versus incentives 
only.58,161,162 Across all of these permutations of combined intervention vs. single intervention, the maximum 
number of participants needed per condition to detect a significant difference between incentives or case 
management alone vs incentives and case management in combination in treatment adherence with 80% 
power was 50, again using the likelihood ratio test for two proportions in SAS POWER. 
In the literature, studies on the use of case management or incentives for adherence to other health-related 
behaviors have demonstrated effect sizes comparable to the studies referenced earlier,66,67,106,107 providing 
additional evidence that those prior studies are appropriate for estimating sample sizes for the proposed 
study. Thus, we estimate that 57 participants per treatment condition (i.e., case management only, 
incentives only, and case management with incentives) and 38 participants in the usual care condition will 
allow us to conduct all pre-planned pairwise comparisons of the primary outcome measure (program 
adherence) with 80% power. 
Randomization 
Stratified random allocation of participants will be used to assign individuals to conditions, using two 
dichotomous variables to stratify the sample: age (<57 years of age vs. >=57) and smoking status (current 
smoker vs. former/non-smoker). Age and current smoking were the two demographic variables which 
significantly predicted CR attendance in the prior trial. As median age in our prior trial was 57, and 40% of 
participants were current smokers, stratifying using the proposed cut-offs will result in randomization cell 
sizes of appropriate size. Participants will be allocated to conditions using a 3:3:3:2 routine, whereby for 
every three individuals assigned to one of the three treatment conditions (case management only, 
incentives only, and case management plus incentives), two individuals will be assigned to the control 
condition (usual care). Assignment of participants to conditions will be conducted using the stratified block 
design method163 in the 

Interactive Voice Recognition (IVR) system maintained by the Medical Biostatistics Unit at the University of 
Vermont. Using this system will allow us to allocate participants over time, as they become eligible, while still 
maintaining desired arm counts across conditions. 
Primary and Secondary Data Analyses 
Treatment conditions will be compared for differences in baseline demographic characteristics using one-way 
Analysis of Variance (ANOVA) (or a nonparametric alternative, such as the Kruskal-Wallis Test) for continuous 
measures and chi-square tests (or Fisher’s Exact Test) for categorical variables. If specific characteristics differ 
significantly across treatment conditions and are predictive of treatment outcomes, they will be considered as 
covariates in subsequent analyses. Primary analyses will include all subjects randomized to treatment 
conditions independent of early dropout or non-adherence, consistent with an intent-to-treat approach for 
randomized clinical trials.  
The primary outcome measure in this trial will be CR adherence (% completing 36 sessions) compared 
between the control condition and any of the three intervention conditions as well as between the combined 
intervention and either of the interventions delivered alone. Proportions completing all CR sessions will be 
examined across conditions using the test for differences between two population proportions (z), with 95% 
confidence intervals on effect sizes. Cox proportional hazard models will be used to test differences in number 
of CR sessions completed (i.e., time to dropout) between conditions. Purposeful selection of covariates will be 
used to build models.164 These covariates will include baseline characteristics in addition to demographics, 
such as maximal exercise capacity, EF, socio-cognitive status, and quality of life. Variables that discriminate 
between participants by condition at a p-value of 0.25 or less using univariable tests will be included in an initial 
multivariable model. Once a reduced model is determined, discarded variables will be tested as potential 
confounders one at a time. The linearity of continuous variables remaining in the reduced model will be tested 
using the fractional polynomial (fp) procedure in STATA 14.2. The best set of power terms modeling each 
continuous variable will be used for building a final model. Interactions between variables will also be tested in 
the final model, initially one at a time and then including all significant interactions from initial testing. Once a 
final model has been derived, the adequacy of the model will be evaluated by using residual analysis to test 
the proportional hazards assumption, subject-specific diagnostics to measure influence on model fit, and 
overall goodness-of-fit. We will generate estimated hazard ratios with 95% confidence intervals and graphs of 
covariate-adjusted survival functions.  
By definition, primary outcome data will not be missing since it focuses on program participation. For other 
variables, minor amounts of missing data, due to factors other than dropout from the CR program, will be 
ignored, leading to deletion of cases in regression analyses. However, if substantial amounts of missing data of 
key predictor variables exist, we will explore the randomness of missing data using Little’s MCAR (missing 
completely at random) test in SPSS 24. If we cannot reject the null hypothesis, we will proceed to use multiple 
imputation procedures in SAS 9.4 (PROC MI and PROC MIANALYZE) to generate results based on imputed 
data. 
Although this study is powered for our primary outcome of adherence to CR, we will also carefully examine 
improvements in fitness, and other health outcomes (quality of life, maximal exercise capacity, weight, waist 
measurements) between conditions, using the pre-planned comparisons outlined for the primary outcome. 
Additionally, we will examine changes in executive function measures and socio-cognitive status at 4 and 12 
months. Changes in these scores will also be examined for possible gender interactions. Since multiple 
observations for each participant will be obtained, the general analytic approach will consist of analysis of 
covariance (ANCOVA) at 4 months and repeated measures analysis of variance (ANOVA) at 12 months. 
Formal testing will examine the condition by time interaction term to assess differential time changes between 
treatment conditions. Post-hoc comparisons between conditions will be made if significant interactions are 
observed.  
Survival analyses will be conducted once all participants have completed the 36 sessions of their CR program 
(or, alternatively, have dropped out of the program). Preliminary analyses of all other outcome measures will 
occur upon collection of all 4-month measures across all participants. Final analyses of secondary outcome 
measures, using repeated measures procedures, will be conducted upon collection of all 12- month measures 
across all participants. Across all tests, statistical significance will be defined as p < 0.05 (2-tailed).   

Cost Effectiveness Analyses 
Dr. Donald Shepard, a Health Care Economist at Brandeis University, will lead the economic evaluation of the 
program. We will collect data on the costs associated with program implementation from the clinical site and 
from participants at each follow-up interview (including travel costs). Operating costs under each arm will be 
collected through a customized cost assessment tool that Dr. Shepard developed using some of our own 
programmatic data for cardiac rehabilitation and lifestyle modification122 and counseling services.148,165 To 
assess costs to participants (e.g. travel expenses, time spent, and out-of-pocket expenses), we will use a tool 
created for our prior study that adapted the Client Drug Abuse Treatment Cost Analysis Program (DATCAP; 
www.datcap.com/client.htm) for use in cardiac rehabilitation. Direct nonmedical and indirect costs include the 
value of time of participants attending the program, waiting, traveling, or exercising, as well as transportation 
expenses.  
Quality-adjusted life years (QALYs)150 for each condition will be calculated using the health-related quality of 
life (HRQL) measure.133 The ratio of cost per QALY will be modeled incorporating meta-analysis data from 
randomized-controlled trials of CR after a coronary event18,166 to estimate the cost-effectiveness of incentives 
encouraging CR compared to standard CR. This cost-effectiveness model will also include hospitalization 
costs averted and possible increases in ambulatory costs if CR use, associated testing, and ambulatory 
services increase for those in treatment conditions.33 Similar to another recent cost-effectiveness study of CR, 
we will perform the analysis with two contrasting time perspectives – the period of follow-up alone and a 
lifetime perspective – based on a carefully calibrated model of project utilization and costs.14 The former 
provides a more conservative, short-term analysis closely tied to the observed data. The latter provides a long-
run perspective.