Semiparametric Regression Analysis of Interval-Censored Multi-State Data with An Absorbing State

Music City Center 

In studies of chronic diseases, the health status of a subject can often be characterized by a finite number of transient disease states and an absorbing state, such as death. The times of transitions among the transient states are ascertained through periodic examinations and thus interval-censored. The time of reaching the absorbing state is known or right-censored, with the transient state at the previous instant being unobserved. We provide a general framework for analyzing such multi-state data. We formulate the effects of potentially time-dependent covariates on
the multi-state disease process through semiparametric proportional intensity models with random effects. We combine nonparametric maximum likelihood estimation with sieve estimation and develop a stable expectation-maximization algorithm. We establish the asymptotic properties of the proposed estimators and assess the performance of the proposed methods through extensive simulation studies. Finally, we provide an illustration with
a cardiac allograft vasculopathy study.

Multi-state model

Interval censoring

Nonparametric maximum likelihood estimation

Semiparametric efficiency

EM algorithm