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 journal › Journal article › peer-review
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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
C2 - 26013050
VL - 71
SP - 687
EP - 695
JO - Biometrics
JF - Biometrics
SN - 0006-341X
IS - 3
ER -
ID: 140626777