# Bayesian latent time joint mixed effect models for multicohort longitudinal data

## TL;DR

This paper introduces a Bayesian latent time joint mixed effects model to analyze long-term neurodegenerative disease progression from short-term multicohort data, enabling better understanding and intervention strategies.

## Contribution

It presents a novel latent time modeling approach with MCMC estimation for long-term disease dynamics using short-term multicohort data.

## Key findings

- Effective in characterizing disease progression
- Accurate in estimating long-term disease trajectories
- Applicable to Alzheimer's disease data

## Abstract

Characterization of long-term disease dynamics, from disease-free to end-stage, is integral to understanding the course of neurodegenerative diseases such as Parkinson's and Alzheimer's; and ultimately, how best to intervene. Natural history studies typically recruit multiple cohorts at different stages of disease and follow them longitudinally for a relatively short period of time. We propose a latent time joint mixed effects model to characterize long-term disease dynamics using this short-term data. Markov chain Monte Carlo methods are proposed for estimation, model selection, and inference. We apply the model to detailed simulation studies and data from the Alzheimer's Disease Neuroimaging Initiative.