A population-averaged additive subdistribution hazards model is proposed to assess the marginal effects of covariates on the cumulative incidence function and to analyze correlated failure time data subject to competing risks. This approach extends the population-averaged additive hazards model by accommodating potentially dependent censoring due to competing events other than the event of interest. Assuming an independent working correlation structure, an estimating equations approach is outlined to estimate the regression coefficients and a new sandwich variance estimator is proposed. The proposed sandwich variance estimator accounts for both the correlations between failure times and between the censoring times, and is robust to misspecification of the unknown dependency structure within each cluster. We further develop goodness-of-fit tests to assess the adequacy of the additive structure of the subdistribution hazards for the overall model and each covariate. Simulation studies are conducted to investigate the performance of the proposed methods in finite samples. We illustrate our methods using data from the STrategies to Reduce Injuries and Develop confidence in Elders (STRIDE) trial.

翻译：提出一种总体平均加性子分布风险模型，用于评估协变量对累积发生率的边际效应，并分析受竞争风险影响的关联失效时间数据。该方法通过考虑目标事件之外竞争事件导致的潜在依赖删失，扩展了总体平均加法风险模型。在假设独立工作相关结构的前提下，概述了一种估计方程方法来估计回归系数，并提出了一个新的三明治方差估计量。该三明治方差估计量同时考虑了失效时间之间和删失时间之间的相关性，并对每个聚类内未知依赖结构的错误设定具有稳健性。我们进一步开发了拟合优度检验，以评估子分布风险的加法结构对整体模型和每个协变量的充分性。通过模拟研究考察了所提出方法在有限样本下的表现。我们使用“减少伤害并增强老年人信心的策略（STRIDE）”试验数据对所提方法进行了说明。