Taylor L, Zhou X H
University of Washington, Seattle, Washington 98195, USA.
Biometrics. 2009 Mar;65(1):88-95. doi: 10.1111/j.1541-0420.2008.01023.x. Epub 2008 Apr 4.
Randomized clinical trials are a powerful tool for investigating causal treatment effects, but in human trials there are oftentimes 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.
随机临床试验是研究因果治疗效果的有力工具，但在人体试验中，经常存在不依从问题，而标准分析方法，如意向性治疗分析或实际治疗分析，要么忽略这些问题，要么以一种使所得估计量不再是因果效应的方式将其纳入。这些分析方法的一种替代方法是依从者平均因果效应（CACE），它估计在任何分配的治疗下都会依从的亚群体中的平均因果治疗效果。我们关注的是具有交叉治疗不依从（例如，对照组受试者可能接受干预，干预组受试者可能接受对照）和结局无应答的随机临床试验设置。在本文中，我们使用多重填补方法开发了CACE的估计量，多重填补方法已成功应用于各种缺失数据问题，但尚未应用于因果推断的潜在结果设置。我们使用模拟数据在一个简单的设置中研究这些估计量以及竞争程序的有限样本性质。最后，我们使用一项关于流感疫苗有效性的实际随机鼓励设计研究来说明我们的方法。
