Competing risks regression for clustered survival data via the marginal additive subdistribution hazards model

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 as well as the those 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 each covariate, as well as for the overall model. Simulation studies are conducted to investigate the performance of proposed methods in finite samples; and we illustrate our methods by analyzing the STrategies to Reduce Injuries and Develop confidence in Elders (STRIDE) study.