Title: A Bayesian hierarchical model estimating CACE in meta-analysis of randomized clinical trials with noncompliance
Authors: Haitao Chu - University of Minnesota School of Public Health (United States) [presenting]
Jincheng Zhou - University of Minnesota School of Public Health (United States)
James Hodges - University of Minnesota School of Public Health (United States)
Fareed Suri - University of Minnesota (United States)
Abstract: Noncompliance to assigned treatment is a common challenge in analysis and interpretation of randomized clinical trials. The complier average causal effect (CACE) approach provides a useful tool for addressing noncompliance, where CACE is defined as the average difference in potential outcomes for the response in a subpopulation of subjects who comply with their assigned treatments. We present a Bayesian hierarchical model to estimate the CACE in a meta-analysis of randomized clinical trials where compliance may be heterogeneous between studies. Between-study heterogeneity is taken into account with study-specific random effects. The results are illustrated by a re-analysis of a meta-analysis comparing epidural analgesia versus no or other analgesia in labor on the outcome of cesarean section, where noncompliance varied between studies. Finally, we present comprehensive simulations evaluating the performance of the proposed approach, and illustrate the importance of including appropriate random effects and the impact of over- and under-fitting.