title: Joint Models for Multiple Longitudinal Processes and Time-to-event Outcome.
abstract: Joint models are statistical tools for estimating the association between time-to-event and longitudinal outcomes. One challenge to the application of joint models is its computational complexity. Common estimation methods for joint models include a two-stage method, Bayesian and maximum-likelihood methods. In this work, we consider joint models of a time-to-event outcome and multiple longitudinal processes and develop a maximum-likelihood estimation method using the expectation-maximization (EM) algorithm. We assess the performance of the proposed method via simulations and apply the methodology to a data set to determine the association between longitudinal systolic and diastolic blood pressure (BP) measures and time to coronary artery disease (CAD).
authors.affiliation: Biogen, 250 Binney Street, Cambridge, MA 02142.
authors.forename: Lili
authors.initials: L
authors.lastname: Yang
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin.
authors.forename: Menggang
authors.initials: M
authors.lastname: Yu
authors.affiliation: Department of Biostatistics, School of Medicine, Indiana University.
authors.forename: Sujuan
authors.initials: S
authors.lastname: Gao
keywords: EM algorithm; joint models; multiple longitudinal outcomes; simulation; time-to-event outcome