Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical Trials
Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical Trials
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DOI：
10.1111/j.1541-0420.2008.01023.x
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发表时间：
2009-03-01
期刊：
影响因子：
1.9
通讯作者：
Zhou, X. H.
中科院分区：
文献类型：
--
作者：
Taylor, L.;Zhou, X. H.
随机临床试验是研究因果治疗效应的有力工具，但在人体试验中，标准分析（如意向治疗分析或实际治疗分析）会忽略或合并十倍的不依从问题，从而导致被估量不再是因果效应。这些分析的一种替代方法是综合平均因果效应（CACE），该方法估计在任何分配的治疗下依从的亚群中的平均因果治疗效应。我们专注于交叉治疗不依从性的随机临床试验的设置（e。例如，在一个实施例中，对照受试者可以接受干预，干预受试者可以接受对照）和结果无应答。在这篇文章中，我们开发了估计的CACE使用多重插补方法，已成功地应用于各种各样的缺失数据问题，但尚未被应用到潜在的结果设置的因果推理。使用模拟数据，我们调查这些估计的有限样本性质，以及在一个简单的设置中的竞争程序。最后，我们用一个真实的随机激励设计研究流感疫苗的有效性来说明我们的方法。
Randomized clinical trials are a powerful tool for investigating causal treatment effects, but in human trials there are of ten times problems of noncompliance which standard analyses, such as the intention-to-treat or as-treated analysis, either ignore or incorporate in such a way that the resulting estimand is no longer a causal effect. One alternative to these analyses is the complier average causal effect (CACE) which estimates the average causal treatment effect among a subpopulation that would comply under any treatment assigned. We focus on the setting of a randomized clinical trial with crossover treatment noncompliance (e. g., control subjects could receive the intervention and intervention subjects could receive the control) and outcome nonresponse. In this article, we develop estimators for the CACE using multiple imputation methods, which have been successfully applied to a wide variety of missing data problems, but have not yet been applied to the potential outcomes setting of causal inference. Using simulated data we investigate the finite sample properties of these estimators as well as of competing procedures in a simple setting. Finally we illustrate our methods using a real randomized encouragement design study on the effectiveness of the influenza vaccine.