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arXiv · 2003.14118

A flexible adaptive lasso Cox frailty model based on the full likelihood

Maike Hohberg·Andreas Groll
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Abstract

In this work a method to regularize Cox frailty models is proposed that accommodates time-varying covariates and time-varying coefficients and is based on the full instead of the partial likelihood. A particular advantage in this framework is that the baseline hazard can be explicitly modeled in a smooth, semi-parametric way, e.g. via P-splines. Regularization for variable selection is performed via a lasso penalty and via group lasso for categorical variables while a second penalty regularizes wiggliness of smooth estimates of time-varying coefficients and the baseline hazard. Additionally, adaptive weights are included to stabilize the estimation. The method is implemented in R as coxlasso and will be compared to other packages for regularized Cox regression. Existing packages, however, do not allow for the combination of different effects that are accommodated in coxlasso.

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Maike Hohberg, Andreas Groll. 2020-03-31. A flexible adaptive lasso Cox frailty model based on the full likelihood. https://arxiv.org/abs/2003.14118

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