Survival Analysis with Multivariate adaptive Regression Splines

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.