While the 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 when there is lack of overlap in the propensity score distributions. By smoothly down-weighting the units with extreme propensity scores, 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 right-censored survival outcomes remains to be further investigated, especially when the target estimand is defined based on the restricted mean survival time (RMST)-a clinically meaningful summary measure free of the proportional hazards assumption. In this article, we combine propensity score weighting and inverse probability of censoring weighting to estimate the restricted mean counterfactual survival times, and propose computationally-efficient variance estimators. We conduct simulations to compare the performance of IPTW, trimming, and OW in terms of bias, variance, and 95% confidence interval coverage, under various degrees of covariate overlap. Regardless of overlap, we demonstrate the advantage of OW over IPTW and trimming methods in bias, variance, and coverage when the estimand is defined based on RMST.

翻译：虽然倾向得分倒数加权法（IPTW）是处理观测数据治疗比较的常用方法，但当倾向得分分布缺乏重叠时，所得到的估计结果可能会受到偏差和过大方差的影响。通过平滑地降低极端倾向得分的权重，重叠权重法（OW）可以帮助减轻IPTW所面临的偏差和方差问题。尽管理论和模拟结果都支持将OW用于连续型和二元型结果的估计，但在右侧被截尾的生存数据中，尤其在受限平均生存时间（RMST）作为目标估计量而定义的情况下，OW的性能还需要进一步研究，因为RMST是没有比例风险假设的临床上可意义的摘要统计量。在本文中，我们将倾向得分加权和倒数加权进行结合，以估计受限平均对照生存时间，并提出了计算高效的方差估计器。我们进行了模拟，以比较在不同程度的协变量重叠下，倒数加权法、分裁剪法和OW在偏差、方差和95%置信区间覆盖率方面的性能。无论何时RMST作为目标估计量来定义，我们展示了OW相对于IPTW和分裁剪法的优势，无论重叠程度如何，都表现出较小的偏差、方差和覆盖率。