Supporting information may be found in the online version of this article.
Absolute risk regression for competing risks: interpretation, link functions, and prediction†
Article first published online: 2 AUG 2012
Copyright © 2012 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 31, Issue 29, pages 3921–3930, 20 December 2012
How to Cite
Gerds, T. A., Scheike, T. H. and Andersen, P. K. (2012), Absolute risk regression for competing risks: interpretation, link functions, and prediction. Statist. Med., 31: 3921–3930. doi: 10.1002/sim.5459
- Issue published online: 23 NOV 2012
- Article first published online: 2 AUG 2012
- Manuscript Accepted: 8 MAR 2012
- Manuscript Received: 4 AUG 2011
- The Danish Natural Science Research Council. Grant Number: 272-06-0442
- absolute risk;
- competing risk;
- cumulative incidence;
- prediction model;
- regression model
In survival analysis with competing risks, the transformation model allows different functions between the outcome and explanatory variables. However, the model's prediction accuracy and the interpretation of parameters may be sensitive to the choice of link function. We review the practical implications of different link functions for regression of the absolute risk (or cumulative incidence) of an event. Specifically, we consider models in which the regression coefficients β have the following interpretation: The probability of dying from cause D during the next t years changes with a factor exp(β) for a one unit change of the corresponding predictor variable, given fixed values for the other predictor variables. The models have a direct interpretation for the predictive ability of the risk factors. We propose some tools to justify the models in comparison with traditional approaches that combine a series of cause-specific Cox regression models or use the Fine–Gray model. We illustrate the methods with the use of bone marrow transplant data. Copyright © 2012 John Wiley & Sons, Ltd.