A1020
Title: Semiparametric latent-class models for multivariate longitudinal and survival data
Authors: Kin Yau Wong - Hong Kong Polytechnic University (Hong Kong) [presenting]
Donglin Zeng - University of North Carolina at Chapel Hill (United States)
Danyu Lin - University of North Carolina at Chapel Hill (United States)
Abstract: In long-term follow-up studies, data are often collected on repeated measures of multivariate response variables as well as on-time to the occurrence of a certain event. To jointly analyze such longitudinal data and survival time, we propose a general class of semiparametric latent-class models that accommodates a heterogeneous study population with flexible dependence structures between the longitudinal and survival outcomes. We combine nonparametric maximum likelihood estimation with sieve estimation and devise an efficient EM algorithm to implement the proposed approach. We establish the asymptotic properties of the proposed estimators through a novel use of modern empirical process theory, sieve estimation theory, and semiparametric efficiency theory. Finally, we demonstrate the advantages of the proposed methods through extensive simulation studies and provide an application to a motivating cohort study
