Bounds on direct effects in the presence of confounded intermediate variables
Bounds on direct effects in the presence of confounded intermediate variables
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DOI：
10.1111/j.1541-0420.2007.00949.x
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发表时间：
2008-09-01
期刊：
影响因子：
1.9
通讯作者：
Tian, Jin
中科院分区：
文献类型：
--
作者：
Cai, Zhihong;Kuroki, Manabu;Tian, Jin
本文考虑的问题，估计平均控制的直接效应（ACDE）的治疗结果，在中间变量和结果之间存在不可测量的混杂因素。这些混杂因素使得直接效应无法识别，即使在总效应未混杂（因此可识别）的情况下。考夫曼（Kaufman）等人（2005，Statistics in Medicine 24，1683-1702）应用线性编程软件来找到特定数值数据的ACDE的最小和最大可能值。在这篇文章中，我们应用符号Balke-Pearl（1997，Journal of the American Statistical Association 92，1171-1176）线性规划方法，在各种单调性假设下推导出ACDE上界和下界的封闭形式公式。这些普遍的界限使临床实验人员能够以最小的计算工作量从观察到的数据中评估治疗的直接效果，并且它们进一步阐明了直接效果的符号和评估的准确性。
This article considers the problem of estimating the average controlled direct effect (ACDE) of a treatment on an outcome, in the presence of unmeasured confounders between an intermediate variable and the outcome. Such confounders render the direct effect unidentifiable even in cases where the total effect is unconfounded (hence identifiable). Kaufman et al. (2005, Statistics in Medicine 24, 1683-1702) applied a linear programming software to find the minimum and maximum possible values of the ACDE for specific numerical data. In this article, we apply the symbolic Balke-Pearl (1997, Journal of the American Statistical Association 92, 1171-1176) linear programming method to derive closed-form formulas for the upper and lower bounds on the ACDE under various assumptions of monotonicity. These universal bounds enable clinical experimenters to assess the direct effect of treatment from observed data with minimum computational effort, and they further shed light on the sign of the direct effect and the accuracy of the assessments.