Hofner, Benjamin and Hothorn, Torsten and Kneib, Thomas
Variable Selection and Model Choice in Structured Survival Models.
Department of Statistics: Technical Reports, No.43
In many situations, medical applications ask for flexible survival models that allow to extend the classical Cox-model via the
inclusion of time-varying and nonparametric effects. These structured survival models are very flexible but additional
difficulties arise when model choice and variable selection is desired. In particular, it has to be decided which covariates
should be assigned time-varying effects or whether parametric modeling is sufficient for a given covariate. Component-wise
boosting provides a means of likelihood-based model fitting that enables simultaneous variable selection and model choice. We
introduce a component-wise likelihood-based boosting algorithm for survival data that permits the inclusion of both parametric
and nonparametric time-varying effects as well as nonparametric effects of continuous covariates utilizing penalized splines as
the main modeling technique. Its properties
and performance are investigated in simulation studies.
The new modeling approach is used to build a flexible survival model for
intensive care patients suffering from severe sepsis.
A software implementation is available to the interested reader.