Parametric likelihood inference for interval censored competing risks data.

ABSTRACT: Parametric estimation of the cumulative incidence function (CIF) is considered 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, extending earlier work on nonparametric estimation. A simple naive likelihood 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 likelihood is shown to be valid under mixed case interval censoring, but not under an independent inspection process model, in contrast w

SUBMITTER: Hudgens MG 

PROVIDER: S-EPMC4004384 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature