  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.
