A Bayesian Approach to Multi-State Hidden Markov Models: Application to Dementia Progression

TL;DR

This paper develops a hierarchical Bayesian multi-state hidden Markov model to analyze dementia progression using longitudinal biomarker data, addressing delayed enrollment bias and providing a flexible, software-supported inference framework.

Contribution

It introduces a novel Bayesian method for multi-state HMMs with time-inhomogeneous parameters and response functions, applicable to disease progression studies.

Findings

Effective modeling of dementia progression from biomarker data

Correction of bias due to delayed enrollment in longitudinal studies

Provision of open-source software for model estimation

Abstract

People are living longer than ever before, and with this arises new complications and challenges for humanity. Among the most pressing of these challenges is of understanding the role of aging in the development of dementia. This paper is motivated by the Mayo Clinic Study of Aging data for 4742 subjects since 2004, and how it can be used to draw inference on the role of aging in the development of dementia. We construct a hidden Markov model (HMM) to represent progression of dementia from states associated with the buildup of amyloid plaque in the brain, and the loss of cortical thickness. A hierarchical Bayesian approach is taken to estimate the parameters of the HMM with a truly time-inhomogeneous infinitesimal generator matrix, and response functions of the continuous-valued biomarker measurements are cut-point agnostic. A Bayesian approach with these features could be useful in many disease progression models. Additionally, an approach is illustrated for correcting a common bias in delayed enrollment studies, in which some or all subjects are not observed at baseline. Standard software is incapable of accounting for this critical feature, so code to perform the estimation of the model described below is made available online.