An Overview and Recent Developments in the Analysis of Multistate Processes
Methodology
2025-02-19 v1
Abstract
Multistate models offer a powerful framework for studying disease processes and can be used to formulate intensity-based and more descriptive marginal regression models. They also represent a natural foundation for the construction of joint models for disease processes and dynamic marker processes, as well as joint models incorporating random censoring and intermittent observation times. This article reviews the ways multistate models can be formed and fitted to life history data. Recent work on pseudo-values and the incorporation of random effects to model dependence on the process history and between-process heterogeneity are also discussed. The software available to facilitate such analyses is listed.
Cite
@article{arxiv.2502.06492,
title = {An Overview and Recent Developments in the Analysis of Multistate Processes},
author = {Malka Gorfine and Richard J. Cook and Per Kragh Andersen and Terry M. Therneau and Pierre Joly and Hein Putter and Maja Pohar Perme and Michal Abrahamowicz},
journal= {arXiv preprint arXiv:2502.06492},
year = {2025}
}
Comments
62 pages, 3 figures, 3 tables