Abstract:Cox models with time‐dependent coefficients and covariates are widely used in survival analysis. In high‐dimensional settings, sparse regularization techniques are employed for variable selection, but existing methods for time‐dependent Cox models lack flexibility in enforcing specific sparsity patterns (ie, covariate structures). We propose a flexible framework for variable selection in time‐dependent Cox models, accommodating complex selection rules. Our method can adapt to arbitrary grouping structures, inc… Show more
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