Latent classification of time-dependent transition rates in longitudinal binary outcome data

Authors

Joonha Chang, LSU Health Sciences Center - New OrleansFollow
Wenyaw Chan, University of Texas Health Science Center at Houston, Houston, TX

Document Type

Article

Publication Date

11-13-2025

Publication Title

Statistical Methods in Medical Research

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.

PubMed ID

41232074

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Recommended Citation

Chang, Joonha and Chan, Wenyaw, "Latent classification of time-dependent transition rates in longitudinal binary outcome data" (2025). School of Public Health Faculty Publications. 533.
https://digitalscholar.lsuhsc.edu/soph_facpubs/533
10.1177/09622802251393610