Application of SHAP values for inferring the optimal functional form of covariates in pharmacokinetic modeling

ABSTRACT: Abstract In population pharmacokinetic (PK) models, interindividual variability is explained by implementation of covariates in the model. The widely used forward stepwise selection method is sensitive to bias, which may lead to an incorrect inclusion of covariates. Alternatives, such as the full fixed effects model, reduce this bias but are dependent on the chosen implementation of each covariate. As the correct functional forms are unknown, this may still lead to an inaccurate selection of covariates. Machine learning (ML) techniques can potentially be used to learn the optimal functional forms for implementing covariates directly from data. A recent study suggested that using ML resulted in an improved selection of influential covariates. However, how do we select the appropriate functi

SUBMITTER: Janssen A 

PROVIDER: S-EPMC9381890 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature