An Overview and Recent Developments in the Analysis of Multistate Processes

TL;DR

This paper reviews multistate models used in disease process analysis, discussing recent developments in modeling techniques, dependence structures, and available software tools for fitting these models to life history data.

Contribution

It provides a comprehensive overview of recent advances in multistate process analysis, including methods for dependence modeling and software resources.

Findings

Discussion of pseudo-values and random effects in multistate models

Review of methods for fitting models to life history data

Listing of software tools for multistate analysis

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.