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在关于混杂参数的假设下潜在风险和因果风险差异的界限。

Bounds on potential risks and causal risk differences under assumptions about confounding parameters.

作者信息

Chiba Yasutaka, Sato Tosiya, Greenland Sander

机构信息

Department of Biostatistics, Kyoto University School of Public Health, Kyoto, Japan.

出版信息

Stat Med. 2007 Dec 10;26(28):5125-35. doi: 10.1002/sim.2927.

Abstract

Nonparametric bounds on causal effects in observational studies are available under deterministic potential-outcome models. We derive narrower bounds by adding assumptions regarding bias due to confounding. This bias is defined as the difference between the expectation of potential outcomes for the exposed group and that for the unexposed group. We show that crude effect measures bound causal effects under the given assumptions. We then derive bounds for randomized studies with noncompliance, which are given by the per protocol effect. With perfect compliance in one treatment group, the direction of effect becomes identifiable under our assumptions. Although the assumptions are not themselves identifiable, they are nonetheless reasonable in some situations.

摘要

在确定性潜在结果模型下,可以得到观察性研究中因果效应的非参数界。我们通过添加关于混杂偏差的假设来推导出更窄的界。这种偏差被定义为暴露组潜在结果的期望与未暴露组潜在结果的期望之间的差异。我们表明,在给定假设下,粗略效应量界定了因果效应。然后,我们推导出了存在不依从情况的随机研究的界,这些界由符合方案效应给出。在一个治疗组中完全依从的情况下,根据我们的假设,效应方向变得可识别。尽管这些假设本身不可识别,但在某些情况下它们仍然是合理的。

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