In this paper we address the challenges posed by non-proportional hazards and informative censoring, offering a path toward more meaningful causal inference conclusions. We start from the marginal structural Cox model, which has been widely used for analyzing observational studies with survival outcomes, and typically relies on the inverse probability weighting method. The latter hinges upon a propensity score model for the treatment assignment, and a censoring model which incorporates both the treatment and the covariates. In such settings, model misspecification can occur quite effortlessly, and the Cox regression model's non-collapsibility has historically posed challenges when striving to guard against model misspecification through augmentation. We introduce an augmented inverse probability weighted estimator which, enriched with doubly robust properties, paves the way for integrating machine learning and a plethora of nonparametric methods, effectively overcoming the challenges of non-collapsibility. The estimator extends naturally to estimating a time-average treatment effect when the proportional hazards assumption fails. We closely examine its theoretical and practical performance, showing that it satisfies both the assumption-lean and the well-specification criteria discussed in the recent literature. Finally, its application to a dataset reveals insights into the impact of mid-life alcohol consumption on mortality in later life.

翻译：本文针对非比例风险和信息删失带来的挑战，提出了解决方案，为获得更具意义的因果推断结论提供了路径。我们从广泛用于分析生存结果的观察性研究的边缘结构Cox模型出发，该模型通常依赖逆概率加权方法。后者依赖于治疗分配的倾向性评分模型以及结合治疗和协变量的删失模型。在此类设定中，模型误设极易发生，而Cox回归模型的非可压缩性在通过增广方法抵御模型误设方面历来构成挑战。我们引入了一种增强型逆概率加权估计量，该估计量具备双重稳健特性，为整合机器学习及众多非参数方法铺平了道路，有效克服了非可压缩性的难题。当比例风险假设不成立时，该估计量可自然地推广至估计时间平均治疗效果。我们对其理论与实际表现进行了深入分析，证明其同时满足近期文献中讨论的模型假设稀疏准则与良好设定准则。最后，将该方法应用于实际数据集，揭示了中年饮酒对晚年死亡率的影响。