Scalable Algorithms for Large Competing Risks Data.

ABSTRACT: This paper develops two orthogonal contributions to scalable sparse regression for competing risks time-to-event data. First, we study and accelerate the broken adaptive ridge method (BAR), a surrogate ℓ ₀-based iteratively reweighted ℓ ₂-penalization algorithm that achieves sparsity in its limit, in the context of the Fine-Gray (1999) proportional subdistributional hazards (PSH) model. In particular, we derive a new algorithm for BAR regression, named cycBAR, that performs cyclic update of each coordinate using an explicit thresholding formula. The new cycBAR algorithm effectively avoids fitting multiple reweighted ℓ ₂-penalizations and thus yields impressive speedups over the original BAR algorithm. Second, we address a pivotal comput

SUBMITTER: Kawaguchi ES 

PROVIDER: S-EPMC9385160 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature