Estimating substance use disparities across intersectional social positions using machine learning: An application of group-lasso interaction network.

ABSTRACT:

Objective

An aim of quantitative intersectional research is to model the joint impact of multiple social positions on health risk behaviors. Although moderated multiple regression is frequently used to pursue intersectional research hypotheses, such parametric approaches may produce unreliable effect estimates due to data sparsity and high dimensionality. Machine learning provides viable alternatives, offering greater flexibility in evaluating many candidate interactions amid sparse data conditions, yet remains rarely employed. This study introduces group-lasso interaction network (glinternet), a novel machine learning approach involving hierarchical regularization, to assess intersectional differences in substance use prevalence.

Method

Utilizing variable selection and para

SUBMITTER: McCabe CJ 

PROVIDER: S-EPMC11671612 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature