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 the 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 the effect of epidural analgesia in labor versus no or other analgesia in labor on the outcome cesarean section, where noncompliance varied between studies. Finally, we present 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.
|Original language||English (US)|
|Number of pages||10|
|State||Published - Sep 1 2019|
Bibliographical noteFunding Information:
The authors thank the Co-Editor, the Associate Editor, and two anonymous reviewers for many helpful comments. This research was supported in part by NIH NLM R21012197 (HC, JZ), NLM R21012744 (HC, JH), AHRQ R03HS024743 (HC), and NIDDK U01 DK106786 (HC).
© 2019 International Biometric Society
- Bayesian hierarchical model
- causal effect
- randomized trial