Zhou Jincheng, Hodges James S, Suri M Fareed K, Chu Haitao
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, 55455.
Department of Neurology, University of Minnesota, Minneapolis, Minnesota, 55455.
Biometrics. 2019 Sep;75(3):978-987. doi: 10.1111/biom.13028. Epub 2019 Apr 4.
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.
在随机临床试验的分析和解释中，不遵守指定治疗方案是一个常见的挑战。依从者平均因果效应（CACE）方法为解决不依从问题提供了一个有用的工具，其中CACE被定义为在遵守其指定治疗方案的受试者亚群中，潜在结果对反应的平均差异。在本文中，我们提出了一种贝叶斯层次模型，用于在随机临床试验的荟萃分析中估计CACE，其中不同研究之间的依从性可能存在异质性。通过特定研究的随机效应来考虑研究间的异质性。通过对一项荟萃分析的重新分析来说明结果，该荟萃分析比较了分娩时硬膜外镇痛与分娩时不使用或使用其他镇痛方法对剖宫产结局的影响，其中不同研究之间的不依从情况各不相同。最后，我们进行了模拟，评估所提出方法的性能，并说明了纳入适当随机效应的重要性以及过度拟合和拟合不足的影响。
