Selection of effects in Cox frailty models by regularization methods.

ABSTRACT: In all sorts of regression problems, it has become more and more important to deal with high-dimensional data 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 penalization methods. In this article, 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 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, time-constant effects, or be irrelevant. A penalization approach is proposed that is able to distinguish betwe

SUBMITTER: Groll A 

PROVIDER: S-EPMC6261611 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature