New publication: Properties of the full random-effect modeling approach with missing covariate data

In this new publication, about the properties of full random effects models (FREM) and full fixed effects models (FFEM) abilities to handle large amounts of missing covariate data, the results suggest that FREM is an appropriate approach to covariate modeling for datasets with more than trivial degrees of missing covariate data, such as in global health studies or multi-study pharmacometric analyses in late phase drug development settings geared towards regulatory filing.

21.12.2023

In this new publication, about the properties of full random effects models (FREM) and full fixed effects models (FFEM) abilities to handle large amounts of missing covariate data, the results suggest that FREM is an appropriate approach to covariate modeling for datasets with more than trivial degrees of missing covariate data, such as in global health studies or multi-study pharmacometric analyses in late phase drug development settings geared towards regulatory filing.