Doubly Robust Estimation under Covariate Dependent Censoring in Semi-Competing Risks Data
Semi-competing risks occur when individuals may experience a non-terminal event and a terminal event, where the terminal event censors the non-terminal event but not vice versa. In the presence of covariate-dependent censoring, augmented inverse probability of censoring weighting (AIPCW) framework has not been developed for the special setting of semi-competing risks which, unlike competing risks, have asymmetric event time structure and complex estimands. Using semiparametric theory on coarsened data, we carefully develop an AIPCW framework for semi-competing risks. When treatment effects are of interest, we further integrate this approach with augmented inverse probability of treatment weighting (AIPTW), yielding a framework for estimating causal estimands under covariate-dependent censoring. The resulting estimators are shown to be doubly robust. Using the proposed framework, we estimate treatment specific risks of: i) non-terminal event, ii) terminal event without the non-terminal event, and iii) terminal event following the non-terminal event. We evaluate the finite sample performance through simulations, and apply the method to data from the Honolulu Asia Aging Study to assess the causal effects of midlife heavy drinking on late life cognitive impairment and mortality.