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    Rights statement: This is the peer reviewed version of the following article: Titman, A.C. (2015) Transition Probability Estimates for Non-Markov Multi-State Models. Biometrics. DOI:10.111/biom.12349, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/biom.12349/abstract This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

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Transition probability estimates for non-Markov multi-state models

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>12/2015
<mark>Journal</mark>Biometrics
Issue number4
Volume71
Number of pages8
Pages (from-to)1034-1041
Publication statusPublished
Early online date6/07/15
Original languageEnglish

Abstract

Non-parametric estimation of the transition probabilities in multi-state models is considered for non-Markov processes. Firstly, a generalization of the estimator of Pepe et al, 1991 (Statistics in Medicine) is given for a class of progressive multi-state models based on the difference between Kaplan-Meier estimators. Secondly, a general estimator for progressive or non-progressive models is proposed based upon constructed univariate survival or competing risks processes which retain the Markov property. The properties of the estimators and their associated standard errors are investigated through simulation. The estimators are demonstrated on datasets relating to survival and recurrence in patients with colon cancer and prothrombin levels in liver cirrhosis patients.

Bibliographic note

This is the peer reviewed version of the following article: Titman, A.C. (2015) Transition Probability Estimates for Non-Markov Multi-State Models. Biometrics. DOI:10.111/biom.12349, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/biom.12349/abstract. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving