An Inverse Probability Weighted Regression Method that Accounts for Right-censoring for Causal Inference with Multiple Treatments and a Binary Outcome
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处理右删失的多处理二元结局逆概率加权回归因果估计
- 研究问题
- 多种处理的观察性比较中，如何同时处理混杂与随访右删失，估计给定时间窗内事件发生风险的平均处理效应？
- 方法与思路
- 作者提出 CIPWR，以包含处理和删失信息的加权得分拟合逻辑回归，再平均预测结局以构造平均处理效应估计。
- 材料陈述的贡献
- 摘要称结局模型正确，或处理与删失模型均正确时可保持一致性，并建立渐近推断性质，开展模拟及四种药物的应用比较。
- 阅读关注点
- 核对权重、目标风险与时间窗的定义，检查双重稳健性的准确条件、删失识别要求，以及渐近方差对辅助估计的处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未完整说明识别条件和有限样本结果，双重稳健性并非任意模型误设皆可。
原文摘要
Comparative effectiveness research often involves evaluating the differences in the risks of an event of interest between two or more treatments using observational data. Often, the post-treatment outcome of interest is whether the event happens within a pre-specified time window, which leads to a binary outcome. One source of bias for estimating the causal treatment effect is the presence of confounders, which are usually controlled using propensity score-based methods. An additional source of bias is right-censoring, which occurs when the information on the outcome of interest is not completely available due to dropout, study termination, or treatment switch before the event of interest. We propose an inverse probability weighted regression-based estimator that can simultaneously handle both confounding and right-censoring, calling the method CIPWR, with the letter C highlighting the censoring component. CIPWR estimates the average treatment effects by averaging the predicted outcomes obtained from a logistic regression model that is fitted using a weighted score function. The CIPWR estimator has a double robustness property such that estimation consistency can be achieved when either the model for the outcome or the models for both treatment and censoring are correctly specified. We establish the asymptotic properties of the CIPWR estimator for conducting inference, and compare its finite sample performance with that of several alternatives through simulation studies. The methods under comparison are applied to a cohort of prostate cancer patients from an insurance claims database for comparing the adverse effects of four candidate drugs for advanced stage prostate cancer.