Efficient nonparametric estimation of causal effects in randomized trials with noncompliance
Efficient nonparametric estimation of causal effects in randomized trials with noncompliance
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DOI：
10.1093/biomet/asn056
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发表时间：
2009-03-01
期刊：
影响因子：
2.7
通讯作者：
Ten Have, Thomas R.
中科院分区：
文献类型：
--
作者：
Cheng, Jing;Small, Dylan S.;Ten Have, Thomas R.
基于潜在结果框架的因果方法为解决随机试验中的不依从问题提供了一个有用的工具。我们提出了一个新的估计因果治疗效果的随机临床试验不遵守。我们使用的经验似然方法来构建一个配置文件的随机筛似然，并考虑到结果分布的混合结构，使我们的估计是强大的参数分布假设，并提供了大量的有限样本效率增益超过标准的工具变量估计。我们的估计是渐近等价于标准的工具变量估计，它可以应用于结果变量的连续，有序或二进制规模。我们将我们的方法应用于一项随机试验的数据，该试验旨在改善初级保健实践中老年抑郁症患者的抑郁症治疗。
Causal approaches based on the potential outcome framework provide a useful tool for addressing noncompliance problems in randomized trials. We propose a new estimator of causal treatment effects in randomized clinical trials with noncompliance. We use the empirical likelihood approach to construct a profile random sieve likelihood and take into account the mixture structure in outcome distributions, so that our estimator is robust to parametric distribution assumptions and provides substantial finite-sample efficiency gains over the standard instrumental variable estimator. Our estimator is asymptotically equivalent to the standard instrumental variable estimator, and it can be applied to outcome variables with a continuous, ordinal or binary scale. We apply our method to data from a randomized trial of an intervention to improve the treatment of depression among depressed elderly patients in primary care practices.