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Activity Number: 334 - Health Policy Statistics Student Paper Awards
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Health Policy Statistics Section
Abstract #304331
Title: A Bayesian Hierarchical Causal Effect Model Accounting for Incomplete Noncompliance Data in Meta-Analysis
Author(s): Jincheng Zhou* and JIM HODGES and Haitao Chu
Companies: University of Minnesota and UNIVERSITY OF MINNESOTA and University of Minnesota
Keywords: Bayesian methods; causal effect; meta-analysis; missing data; noncompliance; randomized trial

Noncompliance is a common challenge in the analysis of a randomized clinical trial. 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 noncompliance 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) 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.

Authors who are presenting talks have a * after their name.

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