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Updated: Jun 13, 2026
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
The use of variable selection in clinical prediction modeling for binary outcomes: a systematic review
Xinrui Su1, Gareth Ambler1, Nathan Green1
1Department of Statistical Science, UCL, London, UK.
Journal of Clinical Epidemiology
|June 11, 2026
Summary
Despite recommendations against data-driven variable selection in clinical prediction models, nearly all studies use these methods. Common but criticized techniques like univariable selection remain prevalent, while newer methods like LASSO are underutilized.
Area of Science:
- Biostatistics
- Clinical Epidemiology
- Health Informatics
Background:
- Clinical prediction models aid medical decisions but require careful variable selection for interpretability and adoption.
- Data-driven variable selection methods are often employed to refine predictor sets, yet some common approaches face criticism.
Purpose of the Study:
- To systematically review current practices in data-driven variable selection for developing clinical prediction models for binary outcomes using logistic regression.
Main Methods:
- A systematic review of 141 studies published in PubMed between October 1-21, 2024, focusing on prediction models for binary health outcomes.
- Extraction of study characteristics and variable selection methodologies, including univariable selection, backwards elimination, bulk removal (BR), and LASSO.
Main Results:
- Nearly all (140/141) studies utilized data-driven variable selection, predominantly univariable selection (78%).
- Backwards elimination (43%) and bulk removal (41%) were also common, often sequentially applied after univariable selection (32% and 31% respectively).
- Reporting deficiencies were noted, including incomplete predictor lists and unclear method specifications.
Conclusions:
- Despite expert recommendations, data-driven variable selection is nearly universal in developing clinical prediction models.
- Frequently criticized methods like univariable selection and backwards elimination are widely used.
- Underutilization of modern penalized methods like LASSO, which optimize predictive performance, was observed.
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