Lay Coach-Led Early Palliative Care for Underserved Advanced Cancer Caregivers: The Project ENABLE Cornerstone RCT

This hybrid type I randomized effectiveness-implementation trial is designed to answer the question "Is ENABLE Cornerstone efficacious in enhancing caregiver and patient outcomes?" The primary aims are to assess the effectiveness of ENABLE Cornerstone over 24 weeks (Primary and Secondary Aim). The investigators will also explore implementation cost and cost effectiveness, including healthcare utilization (Aim 3) and other potential mediator/moderator effects of the intervention on coping, social support, and preparedness (Exploratory Aim). The study will recruit 206 family caregivers over the age of 21 reporting that they are the primary unpaid provider of support to a close family member or friend who has been newly-diagnosed with an advanced stage cancer within the past 60 days. Half of the participants will be randomized to ENABLE Cornerstone (n=106) which consists of 6 brief, weekly in-person/telephone sessions followed by monthly follow-up every 4 weeks as needed by participants. The other half will be assigned to a usual care condition (n=106). The randomization scheme, stratified by site (UAB and MCI) in block lengths of 8, will be executed in REDCap, a clinical trials management software program using a computer-generated algorithm. Assessments will be administered via telephone, by a research coordinator blind to group assignment. Participants will complete outcome assessments upon enrollment (T1) and at 12 (T2), 24 (T3) and every 12 weeks thereafter. The T2 assessment captures the short-term outcomes of the most intensive part of the intervention. The T3 assessment will capture the long-term outcomes of the intervention. Subsequent assessments explore the sustained effect of ENABLE Cornerstone at the patient's end-of-life and post death. The primary outcome is whether the intervention affects the "slope" of FCG distress at 24 weeks. An intention to treat (ITT) approach will be used for all analyses. That is, all caregivers and patients will be included in their respective assigned conditions, regardless of their degree of participation in the study. Primary data analysis will begin with descriptive statistics for baseline caregiver and patient characteristics and outcomes. The investigators will examine balance between study groups with respect to baseline characteristics using effect sizes such as the standardized mean difference for numerical variables and Cramer's V for categorical variables. Conceptually relevant baseline factors showing non-trivial imbalances between groups will be then used as adjusting covariates in the longitudinal group comparisons. Distributional assumptions will be examined and, when appropriate, we will employ inferential and modeling procedures robust to distributional assumptions such as normality. Patterns of missing data will be examined and whether baseline characteristics are associated with dropout. Conceptually relevant baseline factors predictive of dropout will then be used as adjusting covariates in the longitudinal group comparisons. Mixed-effect modeling techniques and covariate adjustment will reduce the impact of missing data, as the missingness is not assumed completely at random (MCAR) but conditionally (on the covariates) at random (i.e., MAR, a milder assumption). We will use the latest versions of SAS and R for all analyses and reports. For specific aims 1 and 2, a longitudinal model fitted with linear mixed methods will be used for each outcome. The focus of inference will be the between-group difference in outcome change from baseline over 12 and 24 weeks, modeled by a time by group interaction. A random effect for subject will be fitted to account for covariance among repeated measures on the same individuals. The fixed-effect part of the model has the form: Y=b\_0+b\_1∙Cornerstone+b\_2∙Week12+b\_3∙Week24+b\_4∙Cornerstone×Week12+b\_5∙Cornerstone×Week24, where Y ̂ is the expected value (i.e., the mean) of the outcome according to the predictor combinations, Cornerstone is a binary indicator of group assignment (1=ENABLE Cornerstone, 0=usual care), and Week24 and Week12 are binary indicators of the follow-up time points. For the usual care group, the mean outcome at baseline is estimated by the b\_0 coefficient, the mean outcome at Week 12 is estimated by b\_0+b\_2, and the mean outcome at Week 24 is estimated by b\_0+b\_3. For the ENABLE Cornerstone group, the mean outcome at baseline is estimated by b\_0+b\_1, the mean outcome at Week 12 is estimated by b\_0+b\_1+b\_2+b\_4, and the mean outcome at Week 24 is estimated by b\_0+b\_1+b\_3+b\_5. The overall treatment effect, ∆, will be computed as the average between-group difference in change from baseline (averaged over Week 12 and Week 24) estimated with a linear contrast, ∆ =(1⁄2)∙(b\_4+b\_5). From the perspective of significance testing, the test for the interaction effect, a 2-degree of freedom test, is the test of difference in change from baseline between the study groups (since the groups are randomized and the mean outcome at baseline should be similar). If necessary, the team will conduct covariate adjustment for baseline factors unbalanced between the groups or predictive of dropout. For specific aim 3, this within-trial analysis will be conducted separately from the perspectives of healthcare payers and FCGs and patients following current recommendations. Because the ENABLE Cornerstone intervention potentially affects FCG and patient distress and QOL and, by consequence, potentially affects healthcare utilization and costs, upfront implementation costs will be weighed against potential savings in healthcare costs and effectiveness measured by improvements in quality of life adjusted years (QALY) over the follow-up period.