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

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2019-01

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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 time-dependent functional and scalar covariates. A Bayesian approach is adopted for parameter estimation and a dynamic prediction framework is introduced for predicting the subjects' future health outcomes and risk of AD conversion. The proposed model is evaluated by a simulation study and is applied to the motivating ADNI study.

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Published Version (Please cite this version)

10.1016/j.csda.2018.07.015

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Li, Kan, and Sheng Luo (2019). Bayesian Functional Joint Models for Multivariate Longitudinal and Time-to-Event Data. Computational statistics & data analysis, 129. pp. 14–29. 10.1016/j.csda.2018.07.015 Retrieved from https://hdl.handle.net/10161/19135.

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Luo

Sheng Luo

Professor of Biostatistics & Bioinformatics

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