ABSOLUTE RISK REGRESSION FOR COMPETING RISKS: INTERPRETATION LINK FUNCTIONS AND PREDICTION

摘要

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 which combine a series of cause-specific Cox regression models, or use the Fine-Gray model. The methods are illustrated using bone marrow transplant data.