Controlled variable selection in Weibull mixture cure models for high-dimensional data.

ABSTRACT: Medical breakthroughs in recent years have led to cures for many diseases. The mixture cure model (MCM) is a type of survival model that is often used when a cured fraction exists. Many have sought to identify genomic features associated with a time-to-event outcome which requires variable selection strategies for high-dimensional spaces. Unfortunately, currently few variable selection methods exist for MCMs especially when there are more predictors than samples. This study develops high-dimensional penalized Weibull MCMs, which allow for identification of prognostic factors associated with both cure status and/or survival. We demonstrated how such models may be estimated using two different iterative algorithms. The model-X knockoffs method was combined with these algorithms to control th

SUBMITTER: Fu H 

PROVIDER: S-EPMC9545322 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature