In causal inference, estimating the average treatment effect is a central objective, and in the context of competing risks data, this effect can be quantified by the cause-specific cumulative incidence function (CIF) difference. While doubly robust estimators give a more robust way to estimate the causal effect from the observational study, they remain inconsistent if both models are misspecified. To improve the robustness, we develop a multiply robust estimator for the difference in cause-specific CIFs using right-censored competing risks data. The proposed framework integrates the pseudo-value approach, which transforms the censored, time-dependent CIF into a complete-data outcome, with the multiply robust estimation framework. By specifying multiple candidate models for both the propensity score and the outcome regression, the resulting estimator is consistent and asymptotically unbiased, provided that at least one of the multiple propensity score or outcome regression models is correctly specified. Simulation studies show our multiply robust estimator remains virtually unbiased and maintains nominal coverage rates under various model misspecification scenarios and a wide range of choices for the censoring rate. Finally, the proposed multiply robust model is illustrated using the Right Heart Catheterization dataset.

翻译：在因果推断中，估计平均处理效应是核心目标，而在竞争风险数据背景下，该效应可通过特定原因累积发生率函数（CIF）的差异进行量化。尽管双稳健估计量提供了一种从观察性研究中更稳健地估计因果效应的方法，但若两个模型均设定错误，它们仍会存在不一致性。为提升稳健性，我们基于右删失竞争风险数据，提出了一种针对特定原因累积发生率函数差异的多重稳健估计量。该框架将伪值方法（将删失的时变累积发生率函数转化为完整数据结果）与多重稳健估计框架相结合。通过为倾向得分和结果回归分别指定多个候选模型，所得估计量在多个倾向得分或结果回归模型中至少有一个被正确设定的条件下，将具有一致性和渐近无偏性。模拟研究表明，在各种模型误设情景及删失率广泛变化的条件下，我们提出的多重稳健估计量几乎保持无偏性，且名义覆盖率维持稳定。最后，利用右心导管插管数据集对本文提出的多重稳健模型进行了实证说明。