Bayesian latent time joint mixed effect models for multicohort longitudinal 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 longterm 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.

Predicting time to dementia using a quantitative template of disease progression

“…Results of experiments investigating the predictive performance of disease age computed using two previously proposed models of AD progression are presented in the lower portion of Table 3. The performance of the disease age computed by Latent Time Joint Mixed effects Model [11] is very similar to that of age. The disease age computed using Gaussian Process Progression Model [15] results in a small RMSE and maximum absolute error, both of which are measures computed using only the individuals who convert; however, the survival curve–based measures, which take into account all individuals regardless of conversion, are poorer.…”

“…In addition to these substantial improvements to our previously described model, our work addresses several limitations of previously described disease progression models. (1) Existing disease progression score models formulated in a Bayesian framework [11,14,15] specify the latent disease progression variable using a single unknown parameter, the time‐shift, meaning that these models do not take into account that disease progression may accelerate over time, limiting their accuracy when working with individual‐level data over long periods of time. Our proposed progression score (PS) takes this phenomenon into account through the use of an additional parameter in the construction of PSs.…”

“…Our proposed progression score (PS) takes this phenomenon into account through the use of an additional parameter in the construction of PSs. (2) Current models either assume independence across biomarkers [15], resulting in biased estimates of latent disease scores [7], or impose linear [11] or double exponential [14] parametric trajectories on biomarkers, limiting the possible functions that can be estimated, potentially resulting in mischaracterization of the time evolution of biomarkers. We model biomarker time courses using flexible monotonic nonlinear functions characterized by basis functions.…”

Bayesian latent time joint mixed‐effects model of progression in the Alzheimer's Disease Neuroimaging Initiative

“…Li et al (2017) [14] proposed the LTJMM to jointly model multivariate longitudinal data. In this work, we employ the LTJMM to provide smooth estimates of the longitudinal course of disease severity, assess the relationship between different markers, and further characterize evolution of disease markers in the progression of disease.…”

“…We apply a latent time joint mixed-effects model (LTJMM) to characterize biomarker trajectories in disease progression [14]. This model extends mixed-effects models to include an individual-specific latent time shift, which is shared across all of an individual's outcomes and represents the extent of their long-term disease progression.…”