Transition probability estimates for non-Markov multi-state models

Titman, Andrew (2015) Transition probability estimates for non-Markov multi-state models. Biometrics, 71 (4). pp. 1034-1041. ISSN 0006-341X

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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.

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Journal Article
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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 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving
Uncontrolled Keywords:
?? multi-state modeltransition probabilitiesnon-parametricnon-markovrobust estimationagricultural and biological sciences(all)biochemistry, genetics and molecular biology(all)applied mathematicsstatistics and probabilityimmunology and microbiology(all)medici ??
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15 Jul 2015 09:20
Last Modified:
31 Dec 2023 00:34