Semiparametric regression analysis of interval-censored competing risks data under additive hazards model with...

Interval-censored competing risks data frequently arise in medical and clinical studies among others and furthermore, the cause of failure may be missing in some situations. In this paper, we consider regression analysis of such data under the framework of an additive subdistribution hazard model and propose a two-step sieve and weighted maximum likelihood estimation procedure. The method explicitly imposes constraints on the cumulative incidence functions to ensure valid survival function estimation and adopts an augmented inverse probability weighting strategy to address the issue of missing event types. Also in the proposed approach, Bernstein polynomials are employed to approximate unknown functions and the proposed estimators are shown to be consistent and asymptotically normal. An extensive simulation study is conducted and indicates that the proposed method works well in practical situations. Finally the proposed approach is applied to the real data from a breast cancer study.