B0186
Title: Effects selection via likelihood-based boosting in Cox frailty models
Authors: Andreas Groll - Technical University Dortmund (Germany) [presenting]
Abstract: In regression tasks, dealing with high-dimensional data has become more and more important with lots of potentially influential covariates. A possible solution is to apply estimation methods that aim at the detection of the relevant effect structure by using regularization methods such as, e.g. boosting or penalization. The effect structure in the Cox frailty model, which is the most widely used model that accounts for heterogeneity in survival data, is investigated. Since in survival models, one has to account for a possible variation of the effect strength over time, the selection of the relevant features has to distinguish between several cases, covariates can have time-varying effects, can have time-constant effects or be irrelevant. A likelihood-based boosting approach is presented, which is able to distinguish between these types of effects to obtain a sparse representation that includes the relevant effects in a proper form. This idea is applied to a real-world data set, illustrating that the complexity of the influence structure can be strongly reduced by using such a regularization approach.
