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Absolute risk regression for competing risks: interpretation, link functions, and prediction
Author(s) -
Gerds Thomas A.,
Scheike Thomas H.,
Andersen Per K.
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5459
Subject(s) - regression , proportional hazards model , regression analysis , econometrics , interpretation (philosophy) , statistics , computer science , linear regression , mathematics , programming language
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.

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