A new joint screening method for right-censored time-to-event data with ultra-high dimensional covariates.

ABSTRACT: In an ultra-high dimensional setting with a huge number of covariates, variable screening is useful for dimension reduction before applying a more refined method for model selection and statistical analysis. This paper proposes a new sure joint screening procedure for right-censored time-to-event data based on a sparsity-restricted semiparametric accelerated failure time model. Our method, referred to as Buckley-James assisted sure screening (BJASS), consists of an initial screening step using a sparsity-restricted least-squares estimate based on a synthetic time variable and a refinement screening step using a sparsity-restricted least-squares estimate with the Buckley-James imputed event times. The refinement step may be repeated several times to obtain more stable results. We show that

SUBMITTER: Liu Y 

PROVIDER: S-EPMC8285086 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature