Semiparametric time-to-event modeling in the presence of a latent progression event.

ABSTRACT: In cancer research, interest frequently centers on factors influencing a latent event that must precede a terminal event. In practice it is often impossible to observe the latent event precisely, making inference about this process difficult. To address this problem, we propose a joint model for the unobserved time to the latent and terminal events, with the two events linked by the baseline hazard. Covariates enter the model parametrically as linear combinations that multiply, respectively, the hazard for the latent event and the hazard for the terminal event conditional on the latent one. We derive the partial likelihood estimators for this problem assuming the latent event is observed, and propose a profile likelihood-based method for estimation when the latent event is unobserved. The

SUBMITTER: Rice JD 

PROVIDER: S-EPMC5325816 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature