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bioRxiv · 10.1101/2021.03.10.434854

Bayesian Multivariate Growth Mixture Modeling of Longitudinal Data: An Application to Alzheimer's Disease Study

Lin, W.·Donohue, M. C.·Insel, P.·Schwartzman, A.·Thompson, W. K.
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Abstract

Alzheimers disease (AD) studies often collect longitudinal biomarker measures of multiple cohorts at different stages of disease and follow these biomarkers with a relatively short period of time. The heterogeneity of the longitudinal patterns of biomarkers can be ubiquitous across both individual trajectories and cognitive domains. We propose a flexible Bayesian multivariate growth mixture model to identify distinct longitudinal patterns of data from the Alzheimers Disease Neuroimaging Initiative (ADNI) study. A Gibbs sampling is implemented for achieving the Bayesian inference. We perform a simulation study to demonstrate the adequate performance of our proposed approach and apply the model to identify three latent cognitive decline patterns among patients from the ADNI study.

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Lin, W., Donohue, M. C., Insel, P., Schwartzman, A., Thompson, W. K.. 2021-03-12. Bayesian Multivariate Growth Mixture Modeling of Longitudinal Data: An Application to Alzheimer's Disease Study. https://doi.org/10.1101/2021.03.10.434854

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