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Updated: Jul 4, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Bootstrap model selection had similar performance for selecting authentic and noise variables compared to backward

Peter C Austin¹

¹Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. peter.austin@ices.on.ca

Journal of Clinical Epidemiology

|June 10, 2008

Summary

This summary is machine-generated.

Bootstrap model selection, a method using resampling to identify predictors, performed comparably to traditional backward elimination for identifying true predictors in regression models.

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Last Updated: Jul 4, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Statistics
Biostatistics
Computational Statistics

Background:

Automated variable selection methods and bootstrap resampling are proposed for identifying outcome predictors and developing parsimonious regression models.
Traditional backward variable elimination is used within bootstrap samples to determine predictor variable selection frequency.
The performance of bootstrap model selection for identifying predictor variables has not been previously examined.

Purpose of the Study:

To evaluate the performance of bootstrap model selection methods in correctly identifying outcome predictors.
To compare the efficacy of bootstrap model selection against conventional backward variable elimination.

Main Methods:

Monte Carlo simulation methods were employed to assess bootstrap model selection.
Variables selected in at least 50% of bootstrap samples were included in the final regression model.
Performance was benchmarked against conventional backward variable elimination.

Main Results:

Bootstrap model selection demonstrated a comparable proportion of selected models matching the true regression model.
The performance of bootstrap model selection was similar to conventional backward variable elimination.

Conclusions:

Bootstrap model selection is a viable alternative to backward variable elimination for identifying true predictors of a binary outcome.
The study suggests comparable performance between bootstrap model selection and backward variable elimination in predictor identification.