Parametric Estimation of Cumulative Incidence Functions for Interval Censored Competing Risks Data

Peter Nyangweso, University of North Carolina at Chapel Hill
Michael Hudgens, University of North Carolina at Chapel Hill
Jason Fine, University of North Carolina at Chapel Hill


We consider parametric estimation of the cumulative incidence function (CIF) for competing risks data subject to interval censoring. Existing parametric models of the CIF for right censored competing risks data are adapted to the general case of interval censoring. Maximum likelihood estimators for the CIF are considered under the assumed models. A simple naive estimator is also considered that utilizes only part of the observed data. The naive estimator enables separate estimation of models for each cause, unlike full maximum likelihood in which all models are fit simultaneously. The naive estimator is shown in a simulation study to perform well relative to the maximum likelihood estimator. The methods are applied to data from a randomized clinical trial for the prevention of mother-to-child transmission of HIV.