Cao Zhiqiang, Ghazi Lama, Mastrogiacomo Claudia, Forastiere Laura, Wilson F Perry, Li Fan
Department of Mathematics, College of Big Data and Internet, Shenzhen Technology University, Guangdong, China.
Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, United States.
Am J Epidemiol. 2025 Aug 5;194(8):2402-2411. doi: 10.1093/aje/kwae416.
Although inverse probability of treatment weighting (IPTW) is a commonly used approach for treatment comparisons in observational data, the resulting estimates may be subject to bias and excessively large variance under lack of overlap. By smoothly down-weighting units with extreme propensity scores (ie, those that are close, or equal, to 0 or 1), overlap weighting (OW) can help mitigate the bias and variance issues associated with IPTW. Although theoretical and simulation results have supported the use of OW with continuous and binary outcomes, its performance with survival outcomes remains to be further investigated, especially when the target estimand is defined based on the restricted mean survival time (RMST). We combine propensity score weighting and inverse probability of censoring weighting to estimate the restricted mean counterfactual survival times, and provide computationally efficient variance estimators when the propensity scores are estimated by logistic regression and the censoring process is estimated by Cox regression. We conduct simulations to compare the performance of weighting methods in terms of bias, variance, and 95% interval coverage, under various degrees of overlap. Under moderate and weak overlap, we demonstrate the advantage of OW over IPTW, trimming and truncation, with respect to bias, variance, and coverage when estimating RMST.
尽管治疗权重逆概率（IPTW）是观察性数据中用于治疗比较的常用方法，但在缺乏重叠的情况下，所得估计值可能会存在偏差且方差过大。通过对倾向得分极端的单元（即接近或等于0或1的单元）进行平滑下加权，重叠加权（OW）有助于减轻与IPTW相关的偏差和方差问题。尽管理论和模拟结果支持将OW用于连续和二元结局，但它在生存结局方面的性能仍有待进一步研究，特别是当目标估计量基于受限平均生存时间（RMST）定义时。我们结合倾向得分加权和删失权重逆概率来估计受限平均反事实生存时间，并在通过逻辑回归估计倾向得分且通过Cox回归估计删失过程时，提供计算效率高的方差估计量。我们进行模拟，以比较在不同重叠程度下加权方法在偏差、方差和95%区间覆盖方面的性能。在中度和弱重叠情况下，我们证明了在估计RMST时，OW在偏差、方差和覆盖方面优于IPTW、修剪和截断。
