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

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 performanc e 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 oper ational characteristics for a statistical backward elimination procedure, defined as ‘fremposthoc’ and to facilitate the comparison to ‘scm’. ‘Fremposthoc’ 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 ‘fremposthoc’, 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 c...
Source: Journal of Pharmacokinetics and Pharmacodynamics - Category: Drugs & Pharmacology Source Type: research