The restricted mean survival time (RMST) difference offers an interpretable causal contrast to estimate the treatment effect for time-to-event outcomes, yet a wide range of available estimators leaves limited guidance for practice. We provide a unified review of RMST estimators for randomized trials and observational studies, establish identification and asymptotic properties, and supply new derivations where needed. Our extensive simulation study compares simple nonparametric methods (such as unweighted Kaplan-Meier estimators) alongside parametric and nonparametric implementations of the G-formula, weighting approaches, Buckley-James transformations, and augmented estimators under diverse censoring mechanisms and model specifications. Across scenarios, classical Kaplan-Meier estimators (weighted when required by the censoring process) and G-formula methods perform well in randomized settings, while in observational data G-formula estimators remain competitive; however, augmented estimators such as AIPTW-AIPCW generally offer robustness to model misspecification and a favorable bias-variance trade-off. Parametric estimators perform best under correct specification, whereas nonparametric methods avoid functional assumptions but require large sample sizes to achieve reliable performance. We offer practical recommendations for estimator choice and provide open-source R code to support reproducibility and application.

翻译：限制平均生存时间（RMST）差异为估计时间至事件结局的处理效应提供了一种可解释的因果对比度量，然而现有的大量估计器在实践中缺乏明确的指导。本文对随机试验和观察性研究中的RMST估计器进行了统一综述，建立了识别条件和渐近性质，并在必要时提供了新的推导。我们通过广泛的模拟研究，比较了简单的非参数方法（如未加权的Kaplan-Meier估计器）与G公式的参数及非参数实现、加权方法、Buckley-James变换以及在不同删失机制和模型设定下的增强估计器。在各种情境中，经典的Kaplan-Meier估计器（在删失过程需要时进行加权）和G公式方法在随机化设定下表现良好，而在观察性数据中G公式估计器仍具竞争力；然而，增强估计器如AIPTW-AIPCW通常对模型误设具有鲁棒性，并提供有利的偏差-方差权衡。参数估计器在模型设定正确时表现最优，而非参数方法避免了函数形式假设，但需要大样本量以实现可靠的性能。我们为估计器选择提供了实用建议，并提供了开源R代码以支持可重复性和应用。