Addressing Extreme Propensity Scores in Estimating Counterfactual Survival Functions via the Overlap Weights
Addressing Extreme Propensity Scores in Estimating Counterfactual Survival Functions via the Overlap Weights
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DOI：
10.1093/aje/kwac043
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发表时间：
2022-03-03
影响因子：
5
通讯作者：
Li, Fan (Frank)
中科院分区：
文献类型：
--
作者：
Cheng, Chao;Li, Fan;Li, Fan (Frank)
在观察性研究中，治疗加权的逆概率（IPTW）方法是评价因果效应的常用方法，但极端倾向评分会使估计量产生偏差，导致方差过大。最近，重叠加权方法已被提出来缓解这个问题，它平滑地向下加权具有极端倾向分数的受试者。尽管重叠加权的优势已在具有连续和二元结果的文献中得到广泛证明，但对其与至事件时间或生存结果的性能的研究有限。在这篇文章中，我们提出了估计，联合收割机倾向评分加权和逆概率截尾加权估计的反事实生存函数。这些估计是适用于一般类的平衡权重，其中包括IPTW，修剪，重叠加权的特殊情况。我们进行模拟，以检查这些估计与不同的倾向评分加权方案的偏倚，方差和95%的置信区间覆盖率方面的实证性能，在不同程度的协变量重叠治疗组和删失率。我们证明，重叠加权始终优于IPTW和相关的修剪方法的偏差，方差和覆盖的时间到事件的结果，和优势增加的程度，治疗组之间的协变量重叠减少。
The inverse probability of treatment weighting (IPTW) approach is popular for evaluating causal 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-weights 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 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 IPTW, trimming, and overlap weighting as special cases. We conduct simulations to examine the empirical performance of these estimators with different propensity score weighting schemes in terms of bias, variance, and 95% confidence interval coverage, under various degrees of covariate overlap between treatment groups and censoring rates. We demonstrate that overlap weighting consistently outperforms IPTW 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.