ArticlePloS one2025
Variable selection methods for descriptive modeling.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Exploring the molecular basis ofBioinformatics advances · 2026Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Variable selection methods are widely used in observational studies. While many penalty-based statistical methods introduced in recent decades have primarily focused on prediction, classical statistical methods remain the standard approach in applied research and education. In this study, we evaluated the variable selection performance of several widely used classical and modern methods for descriptive modeling, using both simulated and real data. A novel aspect of our research is the incorporation of a statistical approach inspired by the supersaturated design-based factor screening method in an observational setting. The methods were evaluated based on Type I and Type II error rates, the average number of predictors selected, variable inclusion frequency, absolute bias, and root mean square error. The detailed results of these evaluations are presented, and the methods' performance is discussed across various simulation scenarios and in application to real data.
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Registered trials
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