NDLI: Covariate Detection in Population Pharmacokinetics Using Partially Linear Mixed Effects Models

Content Provider	Springer Nature Link
Author	Bonate, Peter L.
Copyright Year	2005
Abstract	To introduce partially linear mixed effects models (PLMEMs), to illustrate their use, and to compare the power and Type I error rate in detecting a covariate effect with nonlinear mixed effects modeling using NONMEM.Sparse concentration-time data from males and females (1:1) were simulated under a 1-compartment oral model where clearance was sex-dependent. All possible combinations of number of subjects (50, 75, 100, 150, 250), samples per subject (2, 4, 6), and clearance multipliers (1 to 1.25) were generated. Data were analyzed with and without sex as a covariate using PLMEM (maximum likelihood estimation) and NONMEM (first-order conditional estimation). Four covariate screening methods were examined: NONMEM using the likelihood ratio test (LRT), PLMEM using the LRT, PLMEM using Wald’s test, and analysis of variance (ANOVA) of the empirical Bayes estimates (EBEs) for CL treating sex as a categorical variable. The percent of simulations rejecting the null hypothesis of no covariate effect at the 0.05 level was determined. 300 simulations were done to calculate power curves and 1000 simulations were done (with no covariate effect) to calculate Type I error rate. Actual implementation of PLMEMs is illustrated using previously published teicoplanin data.Type I error rates were similar between PLMEM and NONMEM using the LRT, but were inflated (as high as 36%) based on PLMEM using Wald’s test. Type I error rate tended to increase as the number of observations per subject increased for the LRT methods. Power curves were similar between the PLMEM and NONMEM LRT methods and were slightly more than the power curve using ANOVA on the EBEs of CL. 80% power was achieved with 4 samples per subject and 50 subjects total when the effect size was approximately 1.07, 1.07, 1.08, and 1.05 for LRT using PLMEMs, LRT using NONMEM, ANOVA on the EBEs, and Wald’s test using PLMEMs, respectively.PLMEM and NONMEM covariate screening using the LRT had similar Type I error rates and power under the data generating model. PLMEMs offers a viable alternative to NONMEM-based covariate screening.
Starting Page	541
Ending Page	549
Page Count	9
File Format	PDF
ISSN	07248741
Journal	Pharmaceutical Research
Volume Number	22
Issue Number	4
e-ISSN	1573904X
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2005-04-07
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	linear mixed effects model NONMEM regression splines semiparametric mixed effects models splines Pharmacology/Toxicology Pharmacy Biochemistry Medical Law Biomedical Engineering
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Pharmacology Molecular Medicine Pharmacology (medical) Biotechnology Pharmaceutical Science

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