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：尽管逆概率治疗加权（IPTW）是观察性数据中治疗比较的常用方法，但当倾向得分分布缺乏重叠时，所得估计可能产生偏差且方差过大。通过平滑地降低极端倾向得分单位的权重，重叠加权（OW）有助于缓解IPTW相关的偏差和方差问题。尽管理论和模拟结果已支持OW在连续型和二分类结局中的应用，但其在右删失生存结局中的表现仍需进一步研究，特别是当目标估计量基于受限均数生存时间（RMST）定义时——该指标具有临床意义且无需比例风险假设。本文结合倾向得分加权与逆删失概率加权来估计受限均数反事实生存时间，并提出了计算高效的方差估计量。我们通过模拟研究，在协变量重叠程度不同的条件下，比较了IPTW、截断法和OW在偏差、方差及95%置信区间覆盖率方面的表现。结果表明，无论重叠程度如何，当基于RMST定义估计量时，OW在偏差、方差和覆盖率方面均优于IPTW和截断法。