%0 Journal Article
%T Dynamic predictions in Bayesian functional joint models for longitudinal and time
%A Kan Li
%A Sheng Luo
%J Statistical Methods in Medical Research
%@ 1477-0334
%D 2019
%R 10.1177/0962280217722177
%X In the study of Alzheimer¡¯s disease, researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairment and are diagnosed as having dementia. An accurate prediction of the time to dementia based on the information collected is helpful for physicians to monitor patients¡¯ disease progression and to make early informed medical decisions. In this article, we first propose a functional joint model to account for functional predictors in both longitudinal and survival submodels in the joint modeling framework. We then develop a Bayesian approach for parameter estimation and a dynamic prediction framework for predicting the subjects¡¯ future outcome trajectories and risk of dementia, based on their scalar and functional measurements. The proposed Bayesian functional joint model provides a flexible framework to incorporate many features both in joint modeling of longitudinal and survival data and in functional data analysis. Our proposed model is evaluated by a simulation study and is applied to the motivating Alzheimer¡¯s Disease Neuroimaging Initiative study
%K Alzheimer¡¯s Disease Neuroimaging Initiative study
%K functional data analysis
%K Markov Chain Monte Carlo
%K penalized B-spline
%K personalized prediction
%U https://journals.sagepub.com/doi/full/10.1177/0962280217722177