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Updated: Jun 27, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Purposeful selection of variables in logistic regression.

Zoran Bursac1, C Heath Gauss, David Keith Williams

  • 1Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA. zbursac@uams.edu.

Source Code for Biology and Medicine
|December 18, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces an automated algorithm for covariate selection in statistical modeling. It aids in retaining significant and confounding variables, particularly for risk factor analysis.

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Last Updated: Jun 27, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Variable selection is crucial in model building, with existing methods having limitations.
  • Purposeful selection by analysts offers an alternative but is manual.
  • Automating covariate selection addresses challenges in identifying optimal model variables.

Purpose of the Study:

  • To introduce an automated algorithm for covariate selection in statistical modeling.
  • To compare the performance of the new algorithm against established methods.
  • To provide a tool for analysts needing to retain significant and confounding variables.

Main Methods:

  • An automated covariate selection algorithm was developed.
  • A simulation study was conducted to evaluate the algorithm.
  • Performance was compared against SAS PROC LOGISTIC procedures: FORWARD, BACKWARD, and STEPWISE.

Main Results:

  • The automated algorithm effectively retains significant covariates.
  • It also successfully identifies and retains important confounding variables.
  • This approach is advantageous for risk factor modeling, yielding potentially richer models.

Conclusions:

  • The developed macro offers an alternative tool for automated covariate selection.
  • It assists analysts in retaining both significant and confounding variables.
  • Consider this macro when an automated, guided approach to variable retention is needed.