Faculty, Staff and Student Publications
Language
English
Publication Date
1-1-2026
Journal
Statistical Methods in Medical Research
DOI
10.1177/09622802251393610
PMID
41232074
PMCID
PMC12783371
PubMedCentral® Posted Date
11-13-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Continuous-time Markov chain (CTMC) models and latent classification methods are commonly used to analyze longitudinal categorical outcomes in medical research. While CTMC models are popular for their simplicity and effectiveness, their assumption of constant transition rates presents limitations in capturing dynamic behaviors. To address this, non-homogeneous continuous-time Markov chains (NH-CTMCs) have been developed, incorporating time-varying transition rates to enhance model flexibility. In this study, we leverage closed-form transition probabilities for a fully ergodic two-state NH-CTMC model and propose a latent class clustering approach to identify heterogeneous transition rate patterns within the population. We emphasize the potential advantages of these models in health sciences, particularly for longitudinal studies where transition rates vary over time and across subgroups. Additionally, we demonstrate the practical application of our model using data from an ambulatory hypertension monitoring study.
Keywords
Markov Chains, Longitudinal Studies, Humans, Models, Statistical, Hypertension, Time Factors, Latent Class Analysis, Longitudinal categorical data, continuous-time Markov chain, time-dependent transition modeling, latent class analysis, heterogeneous state transitions
Published Open-Access
yes
Recommended Citation
Joonha Chang and Wenyaw Chan, "Latent Classification of Time-Dependent Transition Rates in Longitudinal Binary Outcome Data" (2026). Faculty, Staff and Student Publications. 1397.
https://digitalcommons.library.tmc.edu/uthsph_docs/1397