Competing Risks Regression for Clustered Data via the Marginal Additive Subdistribution Hazard Model
A population-averaged additive subdistribution hazard model is proposed to assess the marginal effects of covariates on the cumulative incidence function to analyze correlated failure time data subject to competing risks. This approach extends the population-averaged additive hazard 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 considered to estimate the regression coefficients and a sandwich variance estimator is proposed. The 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 hazard for each covariate, as well as for the overall model. Simulation studies are carried out to investigate the performance of the proposed methods in finite samples; and we illustrate our methods by analyzing the STrategies to Reduce Injuries and Develop confidence in Elders (STRIDE) study.