A discrete-time split-state framework for multi-state modeling with application to describing the course of heart disease.

ABSTRACT: In chronic disease epidemiology, the investigation of disease etiology has largely focused on an endpoint, while the course of chronic disease is understudied, representing a knowledge gap. Multi-state models can be used to describe the course of chronic disease, such as Markov models which assume that the future state depends only on the present state, and semi-Markov models which allow transition rates to depend on the duration in the current state. However, these models are unsuitable for chronic diseases that are largely non-memoryless. We propose a Discrete-Time Split-State Framework that generates a process of substates by conditioning on past disease history and estimates discrete-time transition rates between substates as a function of duration in a (sub)state. Specifically, as the

SUBMITTER: Ding M 

PROVIDER: S-EPMC11869649 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature