Assessing the effect of an influenza vaccine in an encouragement design.
Assessing the effect of an influenza vaccine in an encouragement design.
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DOI：
10.1093/biostatistics/1.1.69
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发表时间：
2000-03-01
期刊：
影响因子：
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通讯作者：
Zhou, X H
中科院分区：
文献类型：
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作者：
Hirano, K;Imbens, G W;Zhou, X H
许多随机实验都存在不服从的问题。其中一些实验，即所谓的鼓励设计，可以预期会有特别多的不依从性，因为鼓励接受治疗而不是治疗本身是随机分配给个体的。我们提出了一个扩展的框架，从这样的实验与二进制处理，二进制的鼓励，和背景协变量的数据分析。这个框架有两个关键特征：我们使用工具变量方法将意向治疗效果与治疗效果联系起来，并采用贝叶斯方法进行推断和敏感性分析。这一框架在一个关于流感疫苗接种效果的医学例子中得到了说明。在这个例子中，分析表明，意向治疗效果的积极估计不一定是由于治疗本身，而是由于鼓励接受治疗：无论是否鼓励接种的亚群的意向治疗效应估计大约与接种状态与其接种状态一致的亚群的意向治疗效应一样大。（随机）鼓励状态是否鼓励。因此，我们的方法表明，全球意向治疗估计，虽然通常被认为是保守的，可以过于粗糙，甚至误导时，作为总结的证据数据的治疗效果。
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.