Best Practices for Developing Linear Models With Multiple Explanatory Variables.

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 v

SUBMITTER: Li B 

PROVIDER: S-EPMC12902702 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature