当前位置: X-MOL 学术J. Am. Stat. Assoc. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A Bayesian Hierarchical CACE Model Accounting for Incomplete Noncompliance With Application to a Meta-analysis of Epidural Analgesia on Cesarean Section
Journal of the American Statistical Association ( IF 3.0 ) Pub Date : 2021-04-27 , DOI: 10.1080/01621459.2021.1900859
Jincheng Zhou 1 , James S Hodges 2 , Haitao Chu 2
Affiliation  

Abstract

Noncompliance with assigned treatments is a common challenge in analyzing and interpreting randomized clinical trials (RCTs). One way to handle noncompliance is to estimate the complier-average causal effect (CACE), the intervention’s efficacy in the subpopulation that complies with assigned treatment. In a two-step meta-analysis, one could first estimate CACE for each study, then combine them to estimate the population-averaged CACE. However, when some trials do not report noncompliance data, the two-step meta-analysis can be less efficient and potentially biased by excluding these trials. This article proposes a flexible Bayesian hierarchical CACE framework to simultaneously account for heterogeneous and incomplete noncompliance data in a meta-analysis of RCTs. The models are motivated by and used for a meta-analysis estimating the CACE of epidural analgesia on cesarean section, in which only 10 of 27 trials reported complete noncompliance data. The new analysis includes all 27 studies and the results present new insights on the causal effect after accounting for noncompliance. Compared to the estimated risk difference of 0.8% (95% CI: –0.3%, 1.9%) given by the two-step intention-to-treat meta-analysis, the estimated CACE is 4.1% (95% CrI: –0.3%, 10.5%). We also report simulation studies to evaluate the performance of the proposed method. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.



中文翻译:

用于剖宫产硬膜外镇痛荟萃分析的不完全不依从性的贝叶斯分层 CACE 模型

摘要

不遵守指定的治疗是分析和解释随机临床试验 (RCT) 的常见挑战。处理不依从性的一种方法是估计依从性平均因果效应 (CACE),即干预在符合指定治疗的亚群中的效果。在两步荟萃分析中,可以首先估计每项研究的 CACE,然后将它们结合起来估计人群平均 CACE。然而,当一些试验没有报告不合规数据时,两步荟萃分析的效率可能会降低,并且可能会因排除这些试验而产生偏差。本文提出了一个灵活的贝叶斯分层 CACE 框架,以同时考虑 RCT 荟萃分析中的异构和不完整的不合规数据。这些模型的动机是用于估计剖宫产硬膜外镇痛的 CACE 的荟萃分析,其中 27 项试验中只有 10 项报告了完整的不依从性数据。新的分析包括所有 27 项研究,结果在考虑了违规行为后对因果效应提出了新的见解。与两步意向治疗荟萃分析给出的 0.8% (95% CI: –0.3%, 1.9%) 的估计风险差异相比,估计的 CACE 为 4.1% (95% CrI: –0.3% , 10.5%)。我们还报告了模拟研究以评估所提出方法的性能。本文的补充材料,包括可用于复制作品的材料的标准化描述,可作为在线补充获得。其中 27 项试验中只有 10 项报告了完整的不依从性数据。新的分析包括所有 27 项研究,结果在考虑了违规行为后对因果效应提出了新的见解。与两步意向治疗荟萃分析给出的 0.8% (95% CI: –0.3%, 1.9%) 的估计风险差异相比,估计的 CACE 为 4.1% (95% CrI: –0.3% , 10.5%)。我们还报告了模拟研究以评估所提出方法的性能。本文的补充材料,包括可用于复制作品的材料的标准化描述,可作为在线补充获得。其中 27 项试验中只有 10 项报告了完整的不依从性数据。新的分析包括所有 27 项研究,结果在考虑了违规行为后对因果效应提出了新的见解。与两步意向治疗荟萃分析给出的 0.8% (95% CI: –0.3%, 1.9%) 的估计风险差异相比,估计的 CACE 为 4.1% (95% CrI: –0.3% , 10.5%)。我们还报告了模拟研究以评估所提出方法的性能。本文的补充材料,包括可用于复制作品的材料的标准化描述,可作为在线补充获得。9%)由两步意向治疗荟萃分析得出,估计的 CACE 为 4.1%(95% CrI:–0.3%,10.5%)。我们还报告了模拟研究以评估所提出方法的性能。本文的补充材料,包括可用于复制作品的材料的标准化描述,可作为在线补充获得。9%)由两步意向治疗荟萃分析得出,估计的 CACE 为 4.1%(95% CrI:–0.3%,10.5%)。我们还报告了模拟研究以评估所提出方法的性能。本文的补充材料,包括可用于复制作品的材料的标准化描述,可作为在线补充获得。

更新日期:2021-04-27
down
wechat
bug