The proportional odds cumulative incidence model for competing risks

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The proportional odds cumulative incidence model for competing risks. / Eriksson, Frank; Li, Jianing; Scheike, Thomas; Zhang, Mei-Jie.

In: Biometrics, Vol. 71, No. 3, 09.2015, p. 687–695.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Eriksson, F, Li, J, Scheike, T & Zhang, M-J 2015, 'The proportional odds cumulative incidence model for competing risks', Biometrics, vol. 71, no. 3, pp. 687–695. https://doi.org/10.1111/biom.12330

APA

Eriksson, F., Li, J., Scheike, T., & Zhang, M-J. (2015). The proportional odds cumulative incidence model for competing risks. Biometrics, 71(3), 687–695. https://doi.org/10.1111/biom.12330

Vancouver

Eriksson F, Li J, Scheike T, Zhang M-J. The proportional odds cumulative incidence model for competing risks. Biometrics. 2015 Sep;71(3):687–695. https://doi.org/10.1111/biom.12330

Author

Eriksson, Frank ; Li, Jianing ; Scheike, Thomas ; Zhang, Mei-Jie. / The proportional odds cumulative incidence model for competing risks. In: Biometrics. 2015 ; Vol. 71, No. 3. pp. 687–695.

Bibtex

@article{68ef7832ad5a4200a247872261b0d6ed,
title = "The proportional odds cumulative incidence model for competing risks",
abstract = "We suggest an estimator for the proportional odds cumulative incidence model for competing risks data. The key advantage of this model is that the regression parameters have the simple and useful odds ratio interpretation. The model has been considered by many authors, but it is rarely used in practice due to the lack of reliable estimation procedures. We suggest such procedures and show that their performance improve considerably on existing methods. We also suggest a goodness-of-fit test for the proportional odds assumption. We derive the large sample properties and provide estimators of the asymptotic variance. The method is illustrated by an application in a bone marrow transplant study and the finite-sample properties are assessed by simulations.",
author = "Frank Eriksson and Jianing Li and Thomas Scheike and Mei-Jie Zhang",
note = "{\circledC} 2015, The International Biometric Society.",
year = "2015",
month = "9",
doi = "10.1111/biom.12330",
language = "English",
volume = "71",
pages = "687–695",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "3",

}

RIS

TY - JOUR

T1 - The proportional odds cumulative incidence model for competing risks

AU - Eriksson, Frank

AU - Li, Jianing

AU - Scheike, Thomas

AU - Zhang, Mei-Jie

N1 - © 2015, The International Biometric Society.

PY - 2015/9

Y1 - 2015/9

N2 - We suggest an estimator for the proportional odds cumulative incidence model for competing risks data. The key advantage of this model is that the regression parameters have the simple and useful odds ratio interpretation. The model has been considered by many authors, but it is rarely used in practice due to the lack of reliable estimation procedures. We suggest such procedures and show that their performance improve considerably on existing methods. We also suggest a goodness-of-fit test for the proportional odds assumption. We derive the large sample properties and provide estimators of the asymptotic variance. The method is illustrated by an application in a bone marrow transplant study and the finite-sample properties are assessed by simulations.

AB - We suggest an estimator for the proportional odds cumulative incidence model for competing risks data. The key advantage of this model is that the regression parameters have the simple and useful odds ratio interpretation. The model has been considered by many authors, but it is rarely used in practice due to the lack of reliable estimation procedures. We suggest such procedures and show that their performance improve considerably on existing methods. We also suggest a goodness-of-fit test for the proportional odds assumption. We derive the large sample properties and provide estimators of the asymptotic variance. The method is illustrated by an application in a bone marrow transplant study and the finite-sample properties are assessed by simulations.

U2 - 10.1111/biom.12330

DO - 10.1111/biom.12330

M3 - Journal article

VL - 71

SP - 687

EP - 695

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 3

ER -

ID: 140626777