Toward a Clearer Definition of Selection Bias When Estimating Causal Effects.
Toward a Clearer Definition of Selection Bias When Estimating Causal Effects.
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DOI：
10.1097/ede.0000000000001516
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发表时间：
2022-09-01
期刊：
影响因子：
5.4
通讯作者：
Westreich, Daniel
中科院分区：
文献类型：
--
作者：
Lu, Haidong;Cole, Stephen R.;Howe, Chanelle J.;Westreich, Daniel
关键词：
选择偏差仍然是一个有争议的话题。选择偏差的现有定义是模糊的。为了改善传播和流行病学研究的行为集中在估计因果效应，我们建议统一现有的各种定义的选择偏差在文献中考虑任何偏离真正的因果效应的参考人群（人口之前的选择过程），由于选择样本从参考人群，选择偏差。给定这个统一的定义，选择偏差可以进一步分为两大类型：由于限制到碰撞器（或碰撞器的后代）的一个或多个级别而导致的类型1选择偏差，以及由于限制到效应度量修改器的一个或多个级别而导致的类型2选择偏差。为了帮助解释这两种类型--它们可以同时出现--我们首先回顾一下目标人群、研究样本和分析样本的概念。然后，我们说明这两种类型的选择偏差使用因果图。此外，我们探讨这两种类型的选择偏差之间的差异，并描述方法，以尽量减少选择偏差。最后，我们用一个“M偏误”的例子来说明将选择偏误分为这两种类型的优点。
Selection bias remains a subject of controversy. Existing definitions of selection bias are ambiguous. To improve communication and the conduct of epidemiologic research focused on estimating causal effects, we propose to unify the various existing definitions of selection bias in the literature by considering any bias away from the true causal effect in the referent population (the population prior to the selection process), due to selecting the sample from the referent population, as selection bias. Given this unified definition, selection bias can be further categorized into two broad types: type 1 selection bias due to restricting to one or more level(s) of a collider (or a descendant of a collider), and type 2 selection bias due to restricting to one or more level(s) of an effect measure modifier. To aid in explaining these two types – which can co-occur – we start by reviewing the concepts of the target population, the study sample, and the analytic sample. Then we illustrate both types of selection bias using causal diagrams. In addition, we explore the differences between these two types of selection bias, and describe methods to minimize selection bias. Finally, we use an example of “M-bias” to demonstrate the advantage of classifying selection bias into these two types.