[论文解读] Addressing Extreme Propensity Scores in Estimating Counterfactual Survival Functions via the Overlap Weights

Chao Cheng, Fan Li+2 more|arXiv (Cornell University)|Aug 10, 2021

Advanced Causal Inference Techniques被引用 4

一句话总结

本文提出了一种重叠加权估计器，结合倾向得分加权与删失概率逆加权，以改进在时间至事件结果的观察性研究中反事实生存函数的估计。与标准逆概率加权相比，该方法能有效降低偏差与方差，尤其在处理组间协变量重叠较低时表现更优。

ABSTRACT

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.

研究动机与目标

• 解决在使用观察性数据估计反事实生存函数时极端倾向得分的问题。
• 在治疗组与对照组协变量重叠有限的情况下，提升生存分析的估计准确度与精确度。
• 将此前仅用于连续与二值结果的重叠加权方法，拓展至存在删失的时间至事件结果。
• 在不同协变量重叠程度与删失率下，评估重叠加权相对于逆概率加权与修剪方法的性能表现。

提出的方法

• 提出两种加权估计器，将倾向得分加权与删失概率逆加权相结合，以同时调整治疗分配与删失机制。
• 将广义平衡权重类（包括逆概率加权、修剪与重叠加权）作为特例进行统一处理。
• 采用重叠权重，对倾向得分极端的个体进行平滑降权，以稳定估计过程。
• 使用加权估计方程，以获得反事实生存函数的一致估计量。
• 采用考虑加权结构的方差估计技术，构建有效的置信区间。
• 通过模拟研究，在受控场景下比较不同加权方案的性能表现。

实验结果

研究问题

• RQ1 在协变量重叠程度不同的情况下，重叠加权与逆概率加权及修剪方法在估计反事实生存函数时的表现如何比较？
• RQ2 删失率对不同加权方案在生存结果估计中性能的影响是什么？
• RQ3 当倾向得分为极端值时，重叠加权是否能降低生存函数估计的偏差与方差？
• RQ4 治疗组间协变量重叠程度如何影响不同加权方法的相对表现？
• RQ5 在现实模拟条件下，重叠加权估计器是否能维持足够的95%置信区间覆盖概率？

主要发现

• 在时间至事件结果中，重叠加权在偏差、方差与95%置信区间覆盖方面始终优于逆概率加权与修剪方法。
• 随着治疗组间协变量重叠程度的降低，重叠加权的性能优势更加显著。
• 即使在高删失率条件下，重叠加权仍能保持良好的覆盖性能，而传统方法则表现出更高的覆盖误差。
• 当存在极端倾向得分时，与逆概率加权相比，重叠加权显著降低了偏差与均方误差。
• 所提出的估计器在治疗组间实现了更优的协变量分布平衡，从而产生更可靠的反事实生存估计。
• 模拟结果表明，重叠加权在重叠有限的场景下提供了更稳定、方差更低的估计结果。