Semiparametric regression and risk prediction with competing risks data under missing cause of failure.

ABSTRACT: The cause of failure in cohort studies that involve competing risks is frequently incompletely observed. To address this, several methods have been proposed for the semiparametric proportional cause-specific hazards model under a missing at random assumption. However, these proposals provide inference for the regression coefficients only, and do not consider the infinite dimensional parameters, such as the covariate-specific cumulative incidence function. Nevertheless, the latter quantity is essential for risk prediction in modern medicine. In this paper we propose a unified framework for inference about both the regression coefficients of the proportional cause-specific hazards model and the covariate-specific cumulative incidence functions under missing at random cause of failure. Our ap

SUBMITTER: Bakoyannis G 

PROVIDER: S-EPMC7381366 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature