Nonparametric Bounds and Sensitivity Analysis of Treatment Effects.
Nonparametric Bounds and Sensitivity Analysis of Treatment Effects.
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DOI：
10.1214/14-sts499
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发表时间：
2014-11
期刊：
影响因子：
--
通讯作者：
Fine JP
中科院分区：
文献类型：
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作者：
Richardson A;Hudgens MG;Gilbert PB;Fine JP
本文考虑对治疗（或暴露）对感兴趣结果的影响进行推断。在随机分配治疗的理想环境中，在某些假设下，治疗效果可从可观察数据中识别，推断也很简单。然而，在其他情况下，如观察性研究或不依从的随机试验，如果不依赖于不可检验的假设，治疗效果就不再是可识别的。尽管如此，可观察的数据通常确实提供了一些关于治疗效果的信息，也就是说，感兴趣的参数是部分可识别的。在这种情况下通常采用两种方法：（i）在最小假设下推导治疗效果的界限，或（ii）调用额外的不可检验假设，使治疗效果可识别，然后进行敏感性分析，以评估随着不可检验假设的变化，治疗效果的推断如何变化。方法（一）和（二）是在各种情况下考虑的，包括评估主要的分层效应、直接和间接效应以及随时间变化的照射效应。还讨论了对部分识别参数进行形式化推理的方法。
This paper considers conducting inference about the effect of a treatment (or exposure) on an outcome of interest. In the ideal setting where treatment is assigned randomly, under certain assumptions the treatment effect is identifiable from the observable data and inference is straightforward. However, in other settings such as observational studies or randomized trials with noncompliance, the treatment effect is no longer identifiable without relying on untestable assumptions. Nonetheless, the observable data often do provide some information about the effect of treatment, that is, the parameter of interest is partially identifiable. Two approaches are often employed in this setting: (i) bounds are derived for the treatment effect under minimal assumptions, or (ii) additional untestable assumptions are invoked that render the treatment effect identifiable and then sensitivity analysis is conducted to assess how inference about the treatment effect changes as the untestable assumptions are varied. Approaches (i) and (ii) are considered in various settings, including assessing principal strata effects, direct and indirect effects and effects of time-varying exposures. Methods for drawing formal inference about partially identified parameters are also discussed.