Loux Travis M, Drake Christiana, Smith-Gagen Julie
1 Department of Biostatistics, College for Public Health and Social Justice, Saint Louis University, Saint Louis, USA.
2 Department of Statistics, University of California - Davis, Davis, USA.
Stat Methods Med Res. 2017 Feb;26(1):155-175. doi: 10.1177/0962280214541995. Epub 2016 Sep 30.
Uses of the propensity score to obtain estimates of causal effect have been investigated thoroughly under assumptions of linearity and additivity of exposure effect. When the outcome variable is binary relationships such as collapsibility, valid for the linear model, do not always hold. This article examines uses of the propensity score when both exposure and outcome are binary variables and the parameter of interest is the marginal odds ratio. We review stratification and matching by the propensity score when calculating the Mantel-Haenszel estimator and show that it is consistent for neither the marginal nor conditional odds ratio. We also investigate a marginal odds ratio estimator based on doubly robust estimators and summarize its performance relative to other recently proposed estimators under various conditions, including low exposure prevalence and model misspecification. Finally, we apply all estimators to a case study estimating the effect of Medicare plan type on the quality of care received by African-American breast cancer patients.
在暴露效应的线性和可加性假设下，倾向得分用于获得因果效应估计值的情况已得到充分研究。当结果变量为二元变量时，对于线性模型有效的诸如可折叠性等关系并不总是成立。本文研究当暴露和结果均为二元变量且感兴趣的参数为边际优势比时倾向得分的用途。我们回顾在计算Mantel-Haenszel估计量时按倾向得分进行分层和匹配的情况，并表明它对于边际或条件优势比均不一致。我们还研究了基于双重稳健估计量的边际优势比估计量，并总结了其在各种条件下（包括低暴露患病率和模型错误设定）相对于其他最近提出的估计量的性能。最后，我们将所有估计量应用于一个案例研究，以估计医疗保险计划类型对非裔美国乳腺癌患者所接受护理质量的影响。
