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Covariate detection in population pharmacokinetics using partially linear mixed effects models.
1Genzyme Corp., San Antonio, Texas 78229, USA. peter.bonate@ilexonc.com
Partially linear mixed effects models (PLMEMs) and nonlinear mixed effects modeling (NONMEM) using the likelihood ratio test (LRT) show comparable power and Type I error rates for covariate detection. PLMEMs provide a valid alternative to NONMEM for covariate screening.
Area of Science:
- Pharmacometrics
- Statistical modeling
- Drug development
Background:
- Covariate analysis is crucial in pharmacometric modeling to understand factors influencing drug disposition.
- Nonlinear mixed effects modeling (NONMEM) is a standard tool, but alternative methods are valuable.
- Partially linear mixed effects models (PLMEMs) offer a flexible framework for covariate assessment.
Purpose of the Study:
- To introduce and illustrate the application of partially linear mixed effects models (PLMEMs).
- To compare the statistical power and Type I error rate of PLMEMs against NONMEM for covariate effect detection.
Main Methods:
- Simulated sparse concentration-time data from a 1-compartment model with sex-dependent clearance.
- Analyzed data using PLMEM and NONMEM, employing likelihood ratio tests (LRT) and Wald's test for covariate screening.
- Evaluated Type I error rates (1000 simulations) and power curves (300 simulations) across various design parameters.
Main Results:
- PLMEM and NONMEM using LRT exhibited similar Type I error rates and power.
- PLMEM with Wald's test showed inflated Type I error rates.
- 80% power was achieved with 50 subjects and 4 samples per subject for effective covariate detection.
Conclusions:
- PLMEM and NONMEM, when utilizing the LRT for covariate screening, demonstrate comparable performance.
- PLMEMs represent a viable and effective alternative to NONMEM for covariate screening in pharmacometric analyses.
- The choice of statistical test (LRT vs. Wald's) significantly impacts Type I error rates.
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