Title: A Bayesian hierarchical CACE model accounting for incomplete noncompliance data in meta-analysis
Authors: Jincheng Zhou - University of Minnesota School of Public Health (United States)
James Hodges - University of Minnesota School of Public Health (United States)
Haitao Chu - University of Minnesota School of Public Health (United States) [presenting]
Abstract: Noncompliance to assigned treatments is a common challenge in the analysis and interpretation of a randomized clinical trial (RCT). One approach to handle noncompliance is to estimate the complier-average causal effect (CACE) using the principal stratification framework, where CACE measures the impact of an intervention in the subgroup of the population that complies with its assigned treatment. When non-compliance data are reported in each trial, intuitively one can implement a two-step approach (i.e., first, estimating CACE for each study, and then combining them using a fixed-effect or random effects model) to estimate the population-averaged CACE in a meta-analysis. However, it is common that some trials do not report noncompliance data. The two-step approach can be less efficient and potentially biased as trials with incomplete noncompliance data are excluded. We propose a flexible Bayesian hierarchical CACE framework to simultaneously account for heterogeneous and incomplete noncompliance data in a meta-analysis of RCTs. The performance of the proposed method is evaluated by extensive simulations, and an example of a meta-analysis estimating the CACE of epidural analgesia on cesarean section, in which only 10 out of 27 studies reported complete noncompliance data.