Karlson Kristian Bernt, Popham Frank, Holm Anders
Department of Sociology, University of Copenhagen, Denmark.
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, United Kingdom.
Sociol Methods Res. 2023 Nov;52(4):1765-1784. doi: 10.1177/0049124121995548. Epub 2021 Apr 9.
This article presents two ways of quantifying confounding using logistic response models for binary outcomes. Drawing on the distinction between marginal and conditional odds ratios in statistics, we define two corresponding measures of confounding (marginal and conditional) that can be recovered from a simple standardization approach. We investigate when marginal and conditional confounding may differ, outline why the method by Karlson, Holm, and Breen recovers conditional confounding under a "no interaction"-assumption, and suggest that researchers may measure marginal confounding by using inverse probability weighting. We provide two empirical examples that illustrate our standardization approach.
本文介绍了两种使用二元结果的逻辑响应模型来量化混杂因素的方法。借鉴统计学中边际和条件优势比之间的区别，我们定义了两种相应的混杂因素度量（边际和条件），它们可以通过一种简单的标准化方法得到。我们研究了边际混杂因素和条件混杂因素何时可能不同，概述了卡尔森、霍尔姆和布林的方法在“无交互作用”假设下为何能得到条件混杂因素，并建议研究人员可以使用逆概率加权来度量边际混杂因素。我们提供了两个实证例子来说明我们的标准化方法。
