title: Joint modeling of longitudinal data and discrete-time survival outcome.
abstract: A predictive joint shared parameter model is proposed for discrete time-to-event and longitudinal data. A discrete survival model with frailty and a generalized linear mixed model for the longitudinal data are joined to predict the probability of events. This joint model focuses on predicting discrete time-to-event outcome, taking advantage of repeated measurements. We show that the probability of an event in a time window can be more precisely predicted by incorporating the longitudinal measurements. The model was investigated by comparison with a two-step model and a discrete-time survival model. Results from both a study on the occurrence of tuberculosis and simulated data show that the joint model is superior to the other models in discrimination ability, especially as the latent variables related to both survival times and the longitudinal measurements depart from 0.
authors.affiliation: Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA Medical Mutual of Ohio, Cleveland, OH, USA feiyou.qiu@case.edu.
authors.forename: Feiyou
authors.initials: F
authors.lastname: Qiu
authors.affiliation: Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA Uganda-CWRU Research Collaboration.
authors.forename: Catherine M
authors.initials: CM
authors.lastname: Stein
authors.affiliation: Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.
authors.forename: Robert C
authors.initials: RC
authors.lastname: Elston
mesh_terms: D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D011336:Probability; D016019:Survival Analysis; D013997:Time Factors; D014376:Tuberculosis / Q000175:diagnosis / Q000276:immunology / Q000635:transmission
keywords: Joint modeling; biomarker; discrete time-to-event; immunology; longitudinal; nonlinear; tuberculosis