The Regularized Horseshoe for Covariate Selection Improves Convenience and Predictive Performance in Population PK/PD Models.

ABSTRACT: We introduce the Regularized Horseshoe (RHS) in the context of covariate selection for population PK/PD models. Unlike stepwise approaches which are commonly used in this context, the RHS can simultaneously assess all possible parameter-covariate relationships in a single model fit by leveraging the fact that such relationships are usually sparse in practice. Furthermore, the RHS avoids the over-estimation of effect sizes that commonly occurs with stepwise approaches and avoids overfitting by averaging over the posterior uncertainty of possible parameter-covariate relationships. This leads to improved predictive performance on held-out data. We first give an overview of common covariate selection methods for population PK/PD modeling, then we define the RHS and provide intuition for how th

SUBMITTER: Pourzanjani A 

PROVIDER: S-EPMC12856065 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature