Addressing Extreme Propensity Scores in Estimating Counterfactual Survival Functions via the Overlap Weights
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利用重叠权重处理反事实生存函数估计中的极端倾向得分
- 研究问题
- 观察性研究存在极端倾向得分和结局删失时，如何估计反事实生存函数，并比较不同权重方案在协变量重叠不足时的表现？
- 方法与思路
- 作者提出两种结合倾向得分权重与删失逆概率权重的估计器，覆盖一般平衡权重，并模拟不同重叠程度和删失率。
- 材料陈述的贡献
- 摘要称在所考察模拟中，重叠权重的偏差、方差及置信区间覆盖优于逆概率权重和修剪方法，且重叠较弱时优势更明显。
- 阅读关注点
- 核对各权重对应的目标人群、处理与删失的识别条件，以及两种估计器的区别、区间构造和模拟中模型误设的安排。
- 阅读边界
- 这是 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未给出具体条件和数值，模拟优势不能直接推广到所有生存分析场景。
原文摘要
The inverse probability weighting approach is popular for evaluating treatment effects in observational studies, but extreme propensity scores could bias the estimator and induce excessive variance. Recently, the overlap weighting approach has been proposed to alleviate this problem, which smoothly down-weighs the subjects with extreme propensity scores. Although advantages of overlap weighting have been extensively demonstrated in literature with continuous and binary outcomes, research on its performance with time-to-event or survival outcomes is limited. In this article, we propose two weighting estimators that combine propensity score weighting and inverse probability of censoring weighting to estimate the counterfactual survival functions. These estimators are applicable to the general class of balancing weights, which includes inverse probability weighting, trimming, and overlap weighting as special cases. We conduct simulations to examine the empirical performance of these estimators with different weighting schemes in terms of bias, variance, and 95% confidence interval coverage, under various degree of covariate overlap between treatment groups and censoring rate. We demonstrate that overlap weighting consistently outperforms inverse probability weighting and associated trimming methods in bias, variance, and coverage for time-to-event outcomes, and the advantages increase as the degree of covariate overlap between the treatment groups decreases.