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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 Journal/Magazine › Journal article › peer-review

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Transition probability estimates for non-Markov multi-state models. / Titman, Andrew.
In: Biometrics, Vol. 71, No. 4, 12.2015, p. 1034-1041.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Titman, A 2015, 'Transition probability estimates for non-Markov multi-state models', Biometrics, vol. 71, no. 4, pp. 1034-1041. https://doi.org/10.1111/biom.12349

APA

Titman, A. (2015). Transition probability estimates for non-Markov multi-state models. Biometrics, 71(4), 1034-1041. https://doi.org/10.1111/biom.12349

Vancouver

Titman A. Transition probability estimates for non-Markov multi-state models. Biometrics. 2015 Dec;71(4):1034-1041. Epub 2015 Jul 6. doi: 10.1111/biom.12349

Author

Titman, Andrew. / Transition probability estimates for non-Markov multi-state models. In: Biometrics. 2015 ; Vol. 71, No. 4. pp. 1034-1041.

Bibtex

@article{90b1773de53d4e4fbc263655d2dfbb8e,
title = "Transition probability estimates for non-Markov multi-state models",
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.",
keywords = "multi-state model, Transition probabilities, Non-parametric, Non-Markov, robust estimation",
author = "Andrew Titman",
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",
year = "2015",
month = dec,
doi = "10.1111/biom.12349",
language = "English",
volume = "71",
pages = "1034--1041",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "4",

}

RIS

TY - JOUR

T1 - Transition probability estimates for non-Markov multi-state models

AU - Titman, Andrew

N1 - 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

PY - 2015/12

Y1 - 2015/12

N2 - 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.

AB - 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.

KW - multi-state model

KW - Transition probabilities

KW - Non-parametric

KW - Non-Markov

KW - robust estimation

U2 - 10.1111/biom.12349

DO - 10.1111/biom.12349

M3 - Journal article

VL - 71

SP - 1034

EP - 1041

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 4

ER -