Multivariate adaptive regression splines (MARS) are a useful tool to identify

linear and nonlinear effects and interactions between two covariates. In this dissertation a new proposal to model survival type data with MARS is introduced. Martingale and deviance residuals of a Cox PH model are used as response in a common MARS approach to model functional forms of covariate effects as well as possible interactions in a data-driven way. Simulation studies prove that the new method yields a better fit to the data than the traditional Cox PH approach.

The analysis of real data of the German Heart Center on survivors of an acute myocardial infarction also documents the good performance of the method.