Bayesian Functional Joint Models for Multivariate Longitudinal and Time-to-Event Data.

ABSTRACT: A multivariate functional joint model framework is proposed which enables the repeatedly measured functional outcomes, scalar outcomes, and survival process to be modeled simultaneously while accounting for association among the multiple (functional and scalar) longitudinal and survival processes. This data structure is increasingly common across medical studies of neurodegenerative diseases and is exemplified by the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study, in which serial brain imaging, clinical and neuropsychological assessments are collected to measure the progression of Alzheimer's disease (AD). The proposed functional joint model consists of a longitudinal function-on-scalar submodel, a regular longitudinal submodel, and a survival submodel which allows tim

SUBMITTER: Li K 

PROVIDER: S-EPMC6294314 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature