Hirano K, Imbens G W, Rubin D B, Zhou X H
Department of Economics, University of California, Los Angeles, CA 90095, USA.
Biostatistics. 2000 Mar;1(1):69-88. doi: 10.1093/biostatistics/1.1.69.
Many randomized experiments suffer from noncompliance. Some of these experiments, so-called encouragement designs, can be expected to have especially large amounts of noncompliance, because encouragement to take the treatment rather than the treatment itself is randomly assigned to individuals. We present an extended framework for the analysis of data from such experiments with a binary treatment, binary encouragement, and background covariates. There are two key features of this framework: we use an instrumental variables approach to link intention-to-treat effects to treatment effects and we adopt a Bayesian approach for inference and sensitivity analysis. This framework is illustrated in a medical example concerning the effects of inoculation for influenza. In this example, the analyses suggest that positive estimates of the intention-to-treat effect need not be due to the treatment itself, but rather to the encouragement to take the treatment: the intention-to-treat effect for the subpopulation who would be inoculated whether or not encouraged is estimated to be approximately as large as the intention-to-treat effect for the subpopulation whose inoculation status would agree with their (randomized) encouragement status whether or not encouraged. Thus, our methods suggest that global intention-to-treat estimates, although often regarded as conservative, can be too coarse and even misleading when taken as summarizing the evidence in the data for the effects of treatments.
许多随机试验存在不依从性问题。其中一些试验，即所谓的鼓励性设计，预计会有特别大量的不依从性，因为对接受治疗的鼓励而非治疗本身是随机分配给个体的。我们提出了一个扩展框架，用于分析此类具有二元治疗、二元鼓励和背景协变量的试验数据。该框架有两个关键特征：我们使用工具变量方法将意向性治疗效应与治疗效应联系起来，并且我们采用贝叶斯方法进行推断和敏感性分析。这个框架在一个关于流感接种效果的医学实例中得到了说明。在这个例子中，分析表明意向性治疗效应的正估计值不一定是由于治疗本身，而是由于接受治疗的鼓励：对于无论是否受到鼓励都会接种的亚群体，其意向性治疗效应估计与无论是否受到鼓励其接种状态都与（随机）鼓励状态一致的亚群体的意向性治疗效应大致相同。因此，我们的方法表明，总体意向性治疗估计值虽然通常被视为保守的，但当被用作总结数据中治疗效果的证据时，可能过于粗略甚至具有误导性。
