The impact of misspecified covariate models on inclusion and omission bias when using fixed effects and full random effects models.

ABSTRACT: Identification of covariates that can explain sources of variability among individuals in pharmacometric models is key, as it can lead to patient-subgrouping or patient-specific dosing strategies. Common recommendations propose to limit the covariate-parameters relationships to be tested to those that are scientifically plausible, a process called covariate "scope reduction". We investigated the possible impact of scope reduction on model parameters estimated with misspecified models in terms of omission bias (when a relevant covariate is not included in a model) and inclusion bias (when a non-relevant covariate is included). One-hundred datasets were simulated with a rich-sampling design using 8 variations of a one-compartment model with first-order absorption, having clearance (CL), volu

SUBMITTER: Nyberg J 

PROVIDER: S-EPMC11846770 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature