title: Dynamic predictions using flexible joint models of longitudinal and time-to-event data.
abstract: Joint models for longitudinal and time-to-event data are particularly relevant to many clinical studies where longitudinal biomarkers could be highly associated with a time-to-event outcome. A cutting-edge research direction in this area is dynamic predictions of patient prognosis (e.g., survival probabilities) given all available biomarker information, recently boosted by the stratified/personalized medicine initiative. As these dynamic predictions are individualized, flexible models are desirable in order to appropriately characterize each individual longitudinal trajectory. In this paper, we propose a new joint model using individual-level penalized splines (P-splines) to flexibly characterize the coevolution of the longitudinal and time-to-event processes. An important feature of our approach is that dynamic predictions of the survival probabilities are straightforward as the posterior distribution of the random P-spline coefficients given the observed data is a multivariate skew-normal distribution. The proposed methods are illustrated with data from the HIV Epidemiology Research Study. Our simulation results demonstrate that our model has better dynamic prediction performance than other existing approaches. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
authors.affiliation: Strangeways Research Laboratory, Department of Public Health and Primary Care, University of Cambridge, Worts Causeway, Cambridge, CB1 8RN, U.K.
authors.forename: Jessica
authors.initials: J
authors.lastname: Barrett
authors.affiliation: MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge Robinson Way, Cambridge, CB2 0SR, U.K.
authors.forename: Li
authors.identifier: 0000-0003-0919-3462
authors.initials: L
authors.lastname: Su
mesh_terms: D015415:Biomarkers; D018791:CD4 Lymphocyte Count / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D023381:Endpoint Determination / Q000379:methods; D015658:HIV Infections / Q000401:mortality; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D013997:Time Factors
chemical_list: D015415:Biomarkers
keywords: P-splines; random effects; shared parameter models; survival analysis