Abstract:

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.

Biography:

Dr. Zeng obtained his PhD in Statistics from the University of Michigan in 2001. He is a full professor in the department of Biostatistics at the University of Michigan. Before then, he was a faculty in the department of Biostatistics at the University of North Carolina at Chapel Hill. He is an elected fellow of the American Statistical Association and the Institute of Mathematical Statistics, and a selected member of the International Statistical Institute.  His research interest includes semiparametric/nonparametric inference, high-dimensional data analysis, machine learning and personalized medicine, and he has made important contributions to clinical trials, observational studies, survival analysis, causal inference, and missing data.