Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
Statistical Methods for Big Data, TU Dortmund University, Dortmund, Germany.
Biom J. 2024 Oct;66(7):e202300020. doi: 10.1002/bimj.202300020.
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 likelihood instead of the partial likelihood. A particular advantage of this framework is that the baseline hazard can be explicitly modeled in a smooth, semiparametric way, for example, 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 the R function coxlasso, which is now integrated into the package PenCoxFrail, and will be compared to other packages for regularized Cox regression.
在这项工作中，提出了一种正则化 Cox 脆弱性模型的方法，该方法同时适用于时变协变量和时变系数，并且基于完全似然而不是部分似然。这种框架的一个特别优点是可以以平滑的半参数方式明确地对基线风险进行建模，例如，通过 P-样条。通过lasso 惩罚和分类变量的组lasso 进行变量选择的正则化，同时第二个惩罚正则化时变系数和基线风险的平滑估计的不规则性。此外，还包含自适应权重以稳定估计。该方法在 R 函数 coxlasso 中实现，该函数现在已集成到 PenCoxFrail 包中，并将与其他用于正则化 Cox 回归的包进行比较。
