Outcome Adaptive Propensity Score Methods for Handling Censoring and High-Dimensionality: Application to Insurance Claims
处理删失与高维变量的结局自适应倾向得分方法AI 解读 ↓
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处理删失与高维变量的结局自适应倾向得分方法
- 研究问题
- 在高维治疗前协变量、多治疗组和可能删失的二元结局下，如何利用结局相关信息改善倾向得分及处理效应估计？
- 方法与思路
- 作者将预测的二元结局概率作为倾向得分模型的协变量，并允许结合正则化或树模型等变量选择工具处理高维输入。
- 材料陈述的贡献
- 摘要称模拟中该策略改善效率并缓解模型误设影响；作者将其用于保险理赔数据中四种前列腺癌药物的不良反应比较。
- 阅读关注点
- 应核对结局概率的训练方式、删失处理、多治疗组的效应定义，以及变量选择后的不确定性和模型误设评估。
- 阅读边界
- 这是预印本，不能据此确认同行评审或期刊接收；摘要未说明删失假设及稳健性范围，方法仍不能自动解决未测量混杂。
原文摘要
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.