A principal stratification approach for evaluating natural direct and indirect effects in the presence of treatment-induced intermediate confounding
A principal stratification approach for evaluating natural direct and indirect effects in the presence of treatment-induced intermediate confounding
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DOI：
10.1002/sim.6329
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发表时间：
2015-01-15
影响因子：
2
通讯作者：
Chiba, Yasutaka
中科院分区：
文献类型：
--
作者：
Taguri, Masataka;Chiba, Yasutaka
最近，几位作者表明，只要不存在受治疗影响的中介-结局混杂因素，自然直接和间接效应（NDE和NIE）可以在序贯可验证性假设下识别。然而，如果存在这样的混杂因素，在不进行额外的识别假设的情况下，通常无法识别NDE和NIE。在这篇文章中，我们提出了新的识别假设和估计评估NDE和NIE下通常的顺序可验证性假设，使用主要分层框架。它被假定为治疗和调解人是二分法。我们必须为识别强加强有力的假设。然而，即使这些假设被违反，在典型条件下，我们的估计量的偏差也很小，这可以很容易地从观察到的数据中进行评估。通过推导偏置项的界，证实了这一猜想。此外，我们的估计的优点是通过模拟研究说明。我们还提出了一种敏感性分析的方法，检查当我们的假设被违反时会发生什么。我们将所提出的方法应用于国家卫生统计中心的数据。版权所有（c）2014约翰威利父子有限公司
Recently, several authors have shown that natural direct and indirect effects (NDEs and NIEs) can be identified under the sequential ignorability assumptions, as long as there is no mediator-outcome confounder that is affected by the treatment. However, if such a confounder exists, NDEs and NIEs will generally not be identified without making additional identifying assumptions. In this article, we propose novel identification assumptions and estimators for evaluating NDEs and NIEs under the usual sequential ignorability assumptions, using the principal stratification framework. It is assumed that the treatment and the mediator are dichotomous. We must impose strong assumptions for identification. However, even if these assumptions were violated, the bias of our estimator would be small under typical conditions, which can be easily evaluated from the observed data. This conjecture is confirmed for binary outcomes by deriving the bounds of the bias terms. In addition, the advantage of our estimator is illustrated through a simulation study. We also propose a method of sensitivity analysis that examines what happens when our assumptions are violated. We apply the proposed method to data from the National Center for Health Statistics. Copyright (c) 2014 John Wiley & Sons, Ltd.