A semi-Markov model for survival data with covariates

Abstract

Clinical trials are often concerned with the evaluation of two or more time-dependent stochastic events and their relationship. The information on covariates for individuals in the studies is valuable in assessing the survival function. This paper develops a multistate stochastic survival model which incorporates covariates. It is assumed that the underlying process follows a semi-Markov model. The proportional hazards techniques are applied to estimate the force of transition in the process. The maximum likelihood estimators are derived along with the survival function for competing risks problems. An application is given to analyzing the survival of patients in the Stanford Heart Transplant Program.