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：提出了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相关失效时间数据。该方法通过考虑除目标事件外其他竞争事件导致的潜在相依删失，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，概述了用于估计回归系数的估计方程方法，并提出了新的夹心方差估计量。该夹心方差估计量同时考虑了失效时间之间以及删失时间之间的相关性，并对聚类内未知依赖结构的误设具有稳健性。我们进一步开发了拟合优度检验，以评估子分布风险加法结构对整体模型及每个协变量的适用性。通过模拟研究考察了所提方法在有限样本中的表现，并利用STrategies to Reduce Injuries and Develop confidence in Elders（STRIDE）试验数据进行了实证分析。