论文标题
基于完全可能性的灵活自适应拉索考克斯脆弱模型
A flexible adaptive lasso Cox frailty model based on the full likelihood
论文作者
论文摘要
在这项工作中，提出了一种适应时间变化的协变量和时变系数的方法，以使Cox脆弱的模型正规化，并基于完整的而不是部分可能性。在此框架中的一个特殊优势是，可以以平滑的半参数方式明确对基线危害进行建模，例如通过p-plines。可变选择的正则化是通过套索惩罚和分类变量的组套索进行的，而第二惩罚则正规化了时间变化系数的平滑估计和基线危险的平滑估计。此外，包括自适应权重以稳定估计。该方法在R中作为Coxlasso实现，并将与其他包裹进行比较，以进行正则化的Cox回归。但是，现有包装不允许组合Coxlasso中适应的不同效果。
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.
