A Bayesian Hierarchical Model Estimating CACE in Meta-Analysis of Randomized Clinical Trials with Noncompliance

• Activity Number: 402 - HPSS Student Paper Competition Winners: Statistics Advancing Policy
• Type: Topic Contributed
• Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Jincheng Zhou* and Haitao Chu and James S. Hodges and M. Fareed Khan Suri
• Companies: University of Minnesota and University of Minnesota Twin Cities and University of Minnesota and University of Minnesota
• Keywords: Bayesian hierarchical model; CACE; causal effect; meta-analysis; noncompliance; randomized trial

Abstract

Noncompliance to assigned treatment is a common challenge in the 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. In this article, 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.