Project ENABLE Cornerstone RCT

2020-03-27 03:25:32 | BioPortfolio


Many of the 2.8 million family caregivers (FCGs) of persons with advanced cancer are underserved, particularly African-Americans and rural-dwellers in the Southern U.S.. Most have poor access and awareness of community-based palliative care services and have received no formal support or training despite providing assistance to their relatives an average of 8 hrs/day. Providing intense care and witnessing a close friend or family member struggle with advanced cancer can result in FCGs experiencing marked distress, particularly as their care recipients near end of life (EOL). Reports from NCI and NINR caregiving summits, systematic reviews, and the National Academy of Medicine have highlighted major limitations of cancer caregiver interventions, including a lack of attention to underserved populations and cost, poor scalability, over reliance on highly-trained professionals (e.g., nurses, psychologists, behavioral therapists), lengthy sessions over a short duration, and a lack of demonstrated impact on patient outcomes and healthcare utilization. To address this gap, we have developed and tested feasibility and acceptability of a lay navigator-led early palliative care intervention called ENABLE Cornerstone for rural and minority family caregivers of persons with advanced cancer in the Southern U.S.. Evolving out of our prior trials and community stakeholder formative evaluation work, this multicomponent intervention is based on Pearlin's Stress-Health Process Model where lay navigators, overseen by an interdisciplinary outpatient palliative care team, employ health coaching techniques and caregiver distress screening to behaviorally activate and reinforce psychoeducation on managing stress and coping, getting and asking for help, improving caregiving skills, and decision-making/advance care planning over 6 brief in-person/telephonic sessions plus monthly follow-up from diagnosis through early bereavement. This proposed hybrid type I randomized effectiveness-implementation trial will determine whether ENABLE Cornerstone compared to usual care can improve family caregiver (Aim 1) and patient outcomes (Aim 2) and will evaluate implementation costs, cost effectiveness and healthcare utilization (Aim 3), over 24 weeks with 294 family caregivers and their patients with newly-diagnosed advanced cancer. To maximize recruitment, we will recruit from two community cancer centers in Birmingham, AL and Mobile, AL. Our theory-driven, standardized approach is innovative because it uses lay navigators in collaboration with a palliative care interdisciplinary team to promote caregiver activation, skills and knowledge enhancement, as opposed to other difficult-to-implement intervention models that rely mostly on delivery of services by advanced practice professionals providing lengthy sessions over a short duration. If effectiveness is established, the ENABLE Cornerstone intervention offers a highly scalable and reproducible model of formal caregiver support that would be primed for dissemination and implementation.


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). We 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). We will recruit 294 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=147) 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=147). 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.

We will use an intention to treat (ITT) approach 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. We 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. We will examine patterns of missing data, 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, we 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.

Study Design


Cancer Metastatic


ENABLE Cornerstone


Not yet recruiting


University of Alabama at Birmingham

Results (where available)

View Results


Published on BioPortfolio: 2020-03-27T03:25:32-0400

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