title: Joint modeling of repeated multivariate cognitive measures and competing risks of dementia and death: a latent process and latent class approach.
abstract: Joint models initially dedicated to a single longitudinal marker and a single time-to-event need to be extended to account for the rich longitudinal data of cohort studies. Multiple causes of clinical progression are indeed usually observed, and multiple longitudinal markers are collected when the true latent trait of interest is hard to capture (e.g., quality of life, functional dependency, and cognitive level). These multivariate and longitudinal data also usually have nonstandard distributions (discrete, asymmetric, bounded, etc.). We propose a joint model based on a latent process and latent classes to analyze simultaneously such multiple longitudinal markers of different natures, and multiple causes of progression. A latent process model describes the latent trait of interest and links it to the observed longitudinal outcomes using flexible measurement models adapted to different types of data, and a latent class structure links the longitudinal and cause-specific survival models. The joint model is estimated in the maximum likelihood framework. A score test is developed to evaluate the assumption of conditional independence of the longitudinal markers and each cause of progression given the latent classes. In addition, individual dynamic cumulative incidences of each cause of progression based on the repeated marker data are derived. The methodology is validated in a simulation study and applied on real data about cognitive aging obtained from a large population-based study. The aim is to predict the risk of dementia by accounting for the competing death according to the profiles of semantic memory measured by two asymmetric psychometric tests.
authors.affiliation: INSERM, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, F-33000, Bordeaux, France.|Université de Bordeaux, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, F-33000, Bordeaux, France.
authors.forename: Cécile
authors.initials: C
authors.lastname: Proust-Lima
authors.affiliation: INSERM, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, F-33000, Bordeaux, France.|Université de Bordeaux, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, F-33000, Bordeaux, France.
authors.forename: Jean-François
authors.initials: JF
authors.lastname: Dartigues
authors.affiliation: INSERM, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, F-33000, Bordeaux, France.|Université de Bordeaux, ISPED, Centre INSERM U897-Epidemiologie-Biostatistique, F-33000, Bordeaux, France.
authors.forename: Hélène
authors.initials: H
authors.lastname: Jacqmin-Gadda
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D053327:Apolipoprotein E4 / Q000032:analysis*; D001499:Bayes Theorem; D035843:Biomedical Research / Q000379:methods* / Q000706:statistics & numerical data; D003071:Cognition / Q000145:classification; D003198:Computer Simulation; D003704:Dementia / Q000175:diagnosis*; D018450:Disease Progression; D005260:Female; D005602:France; D005819:Genetic Markers; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D011446:Prospective Studies; D011594:Psychometrics; D018570:Risk Assessment / Q000379:methods* / Q000706:statistics & numerical data; D013997:Time Factors
chemical_list: D053327:Apolipoprotein E4; D005819:Genetic Markers
keywords: competing risk; conditional independence; curvilinearity; joint model; latent class; multivariate longitudinal data