Scientific Director Pharmetheus AB Uppsala, United States
Disclosure(s):
E. Niclas Jonsson, PhD: No financial relationships to disclose
Description of session (include background & scientific importance):
Background: Covariate modeling is essential in pharmacometrics to understand variability in drug response and optimize treatment strategies. However, traditional stepwise selection methods can be limited by covariate correlations and missing data, potentially leading to biased or incomplete results.
Scientific Importance: The full random effects model (FREM) offers a novel approach that addresses these limitations by treating covariates as observations and modeling their impact on individual model parameters through covariances. This allows FREM to:
Account for complex covariate relationships without explicit selection. Robustly handle missing covariate data, reducing bias and improving efficiency. Avoid omission bias, leading to more accurate covariate effect estimation. Facilitate clear communication of results through forest plots and explained variability plots. This presentation will provide an introduction to FREM, exploring its methodology, benefits, and challenges. Examples will demonstrate FREM's practical application and illustrate how it can enhance covariate analysis in pharmacometric research. Strategies for managing the computational complexities associated with FREM will be discussed and it will be demonstrated how it can be implemented in major pharmacometric software packages.
Learning Objectives:
Upon completion of this session, participants will be able to:
- Appreciate the core principles of FREM
- Understand the advantages of FREM over traditional covariate modeling approaches.
- Recognise the challenges involved in the FREM methodology
- Know how to interpret communications of results from a FREM model
