Operational characteristics of full random effects modelling (‘frem’) compared to stepwise covariate modelling (‘scm’)

• Published in: Journal of pharmacokinetics and pharmacodynamics Vol. 50; no. 4; pp. 315 - 326
• Main Authors: Amann, Lisa F., Wicha, Sebastian G.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2023; Springer Nature B.V
• Subjects: Automation; Biochemistry; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Computer Simulation; Humans; Models, Biological; Original Paper; Pharmacokinetics; Pharmacology/Toxicology; Pharmacy; Veterinary Medicine/Veterinary Science; Population pharmacokinetics; Simulation study; Covariate analysis; NONMEM
• ISSN: 1567-567X; 1573-8744
• DOI: 10.1007/s10928-023-09856-w

An adequate covariate selection is a key step in population pharmacokinetic modelling. In this study, the automated stepwise covariate modelling technique (‘scm’) was compared to full random effects modelling (‘frem’). We evaluated the power to identify a ‘true’ covariate (covariate with highest correlation to the pharmacokinetic parameter), precision, and accuracy of the parameter-covariate estimates. Furthermore, the predictive performance of the final models was assessed. The scenarios varied in covariate effect sizes, number of individuals (n = 20–500) and covariate correlations (0–90% cov-corr). The PsN ‘frem’ routine provides a 90% confidence intervals around the covariate effects. This was used to evaluate its operational characteristics for a statistical backward elimination procedure, defined as ‘frem posthoc ’ and to facilitate the comparison to ‘scm’. ‘Frem posthoc ’ had a higher power to detect the true covariate with lower bias in small n studies compared to ‘scm’, applied with commonly used settings (forward p < 0.05, backward p < 0.01). This finding was vice versa in a statistically similar setting. For ‘frem posthoc ’, power, precision and accuracy of the covariate coefficient increased with higher number of individuals and covariate effect magnitudes. Without a backward elimination step ‘frem’ models provided unbiased coefficients with highly imprecise coefficients in small n datasets. Yet, precision was superior to final ‘scm’ model precision obtained using common settings. We conclude that ‘frem posthoc ’ is also a suitable method to guide covariate selection, although intended to serve as a full model approach. However, a deliberated selection of automated methods is essential for the modeller and using those methods in small datasets needs to be taken with caution.