![]() ![]() Non-linear mixed effects (NLME) models consist of both population and subject specific characteristics which are represent fixed parameters for population and random parameters for subjects. Mixed effects models, which are widely used as a flexible and powerful tool, deal with repeated measures data. In many applications, particularly in the biological sciences, the time course of a response for an individual may be characterized by a function that is nonlinear in one or more parameters, where an “individual†may be a human subject, an animal, a plant, an agricultural plot, a laboratory sample or other observational unit. Nonlinear mixed effects models Bioequivalence Noncompartmental analysis Thus, nonlinear mixed effects models approach was suggested as an efficient and alternative analysis tool for bioequivalence studies. In the light of results at this study, nonlinear mixed effects models approach was more effective than non-compartmental analysis. According to real data analysis, nonlinear mixed effects models approach has smaller within subject error, narrower confidence interval and smaller p-value than non-compartmental analysis. ![]() A real data application was provided for the study, which was get from Ege University Drug Development and Pharmacokinetics Research Center. On the other hand, nonlinear mixed effects models approach is more complex than non-compartmental analysis but it has some advantages such as it requires few samples per subject. Non-compartmental analysis requires few hypotheses but a large number of samples per subject. The aim of this study was to investigate the use of nonlinear mixed effects models in biequivalence studies and compare it to non-compartmental analysis which is proposed by regulatory agencies. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received OctoPublished November 05, 2012Ĭitation: Korkmaz S, Orman MN (2012) The Use of Nonlinear Mixed Effects Models in Bioequivalence Studies: A Real Data Application. Orman 2ġ Department of Biostatistics and Medical Informatics, Faculty of Medicine, Trakya University, Merkez, Edirne, TurkeyĢ Department of Biostatistics and Medical Informatics, Faculty of Medicine, Ege University, Bornova, Izmir, Turkeyĭepartment of Biostatistics and Medical Informatics The Use of Nonlinear Mixed Effects Models in Bioequivalence Studies: AReal Data Application ![]()
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