Inference for Non-Markov Multi-State Models

An Overview

Authors

  • Luís Meira-Machado University of Minho

DOI:

https://doi.org/10.57805/revstat.v9i1.99

Keywords:

bivariate censoring, Markov property, multi-state models, Kaplan–Meier, presmoothing, transition probabilities

Abstract

In longitudinal studies of disease, patients can experience several events across a follow-up period. Analysis of such studies can be successfully performed by multistate models. This paper considers nonparametric and semiparametric estimation of important targets in multi-state modeling, such as the transition probabilities and bivariate distribution function (for sequentially ordered events). These estimators are shown to be consistent even for data which is non-Markov. We illustrate the methods on two data sets.

Published

2011-04-07

How to Cite

Meira-Machado , L. (2011). Inference for Non-Markov Multi-State Models: An Overview. REVSTAT-Statistical Journal, 9(1), 83–98. https://doi.org/10.57805/revstat.v9i1.99