A comparison of penalised regression methods for informing the selection of predictive markers.

ABSTRACT:

Background

Penalised regression methods are a useful atheoretical approach for both developing predictive models and selecting key indicators within an often substantially larger pool of available indicators. In comparison to traditional methods, penalised regression models improve prediction in new data by shrinking the size of coefficients and retaining those with coefficients greater than zero. However, the performance and selection of indicators depends on the specific algorithm implemented. The purpose of this study was to examine the predictive performance and feature (i.e., indicator) selection capability of common penalised logistic regression methods (LASSO, adaptive LASSO, and elastic-net), compared with traditional logistic regression and forward selection methods.

De

SUBMITTER: Greenwood CJ 

PROVIDER: S-EPMC7678959 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature