Propensity scores are commonly used to reduce the confounding bias in non-randomized observational studies for estimating the average treatment effect. An important assumption underlying this approach is that all confounders that are associated with both the treatment and the outcome of interest are measured and included in the propensity score model. In the absence of strong prior knowledge about potential confounders, researchers may agnostically want to adjust for a high-dimensional set of pre-treatment variables. As such, variable selection procedure is needed for propensity score estimation. In addition, recent studies show that including variables related to treatment only in the propensity score model may inflate the variance of the treatment effect estimates, while including variables that are predictive of only the outcome can improve efficiency. In this paper, we propose a flexible approach to incorporating outcome-covariate relationship in the propensity score model by including the predicted binary outcome probability (OP) as a covariate. Our approach can be easily adapted to an ensemble of variable selection methods, including regularization methods and modern machine learning tools based on classification and regression trees. We evaluate our method to estimate the treatment effects on a binary outcome, which is possibly censored, among multiple treatment groups. Simulation studies indicate that incorporating OP for estimating the propensity scores can improve statistical efficiency and protect against model misspecification. The proposed methods are applied to a cohort of advanced stage prostate cancer patients identified from a private insurance claims database for comparing the adverse effects of four commonly used drugs for treating castration-resistant prostate cancer.

中文翻译：

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用于处理审查和高维的结果自适应倾向评分方法：在保险索赔中的应用

倾向评分通常用于减少非随机观察研究中的混杂偏倚，以估计平均治疗效果。这种方法的一个重要假设是所有与治疗和感兴趣的结果相关的混杂因素都被测量并包含在倾向评分模型中。在缺乏关于潜在混杂因素的强大先验知识的情况下，研究人员可能会不可知地想要调整一组高维的预处理变量。因此，倾向得分估计需要变量选择过程。此外，最近的研究表明，仅在倾向评分模型中包含与治疗相关的变量可能会夸大治疗效果估计的方差，同时包括仅预测结果的变量可以提高效率。在本文中，我们提出了一种灵活的方法，通过将预测的二元结果概率 (OP) 作为协变量，将结果-协变量关系纳入倾向评分模型。我们的方法可以很容易地适应一组变量选择方法，包括正则化方法和基于分类和回归树的现代机器学习工具。我们评估我们的方法，以估计在多个治疗组中对可能被审查的二元结果的治疗效果。模拟研究表明，将 OP 用于估计倾向得分可以提高统计效率并防止模型错误指定。