ArticleAdvanced genetics (Hoboken, N.J.)2026
Best Practices for Developing Linear Models With Multiple Explanatory Variables.
Article in Advanced genetics (Hoboken, N.J.), 2026. 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.
- Comparative evaluation of dental caries and periodontal conditions and oral health-related quality of life of dental and medical students: a cross-sectional study.BMC medical education · 2026Observational
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2 authors.
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Abstract
Linear models, including t-test, ANOVA, regression, ANCOVA, and generalized linear models, are foundational tools in statistical analysis. For large datasets, such as those involving tens of thousands of genes and millions of records, numerous advanced methods have been developed to improve both computational efficiency and reliability. Here, we focus on a more general scenario: a linear model with many explanatory variables (e.g., >10) and a moderate sample size (e.g., thousands of observations). This paper provides the best practices for model selection, emphasizing the importance of including two-way interaction and quadratic terms, which are frequently overlooked in textbooks and classic literature. When dealing with high-dimensional data, we recommend using random forest for initial variable screening, followed by subset selection methods such as stepwise regression. Model selection can be guided by criteria like AIC, BIC, adjusted R
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