Nonmem population pharmacokinetics 2016 california
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Pharmacokinetics of quinidine in male patients: a population analysis. Population pharmacokinetics of imipramine in children.
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Population pharmacokinetics and pharmacodynamics of thiopental: The effect of age revisited. Estimation of population characteristics of pharmacokinetic parameters from routine clinical data. Population pharmacokinetic analysis of Didanosine (2′, 3′-dideoxyinosine) plasma concentrations obtained in phase I clinical trials in patients with AIDS or AIDS-related complex. Postinduction carbamazepine clearance in an adult psychiatric population. Population pharmacokinetics of mitoxantrone performed by a NONMEM method. Martin III Population pharmacokinetics of lithium. Population pharmacokinetics of gentamicin in neonates using a nonlinear, mixed-effects model. Population pharmacokinetics of gentamicin in premature infants. Population pharmacokinetic analysis of bisoprolol. Population analysis of the pharmacokinetic variability of high-dose metoclopramide in cancer patients.
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Pharmacokinetics of felbamate, a novel antiepileptic drug: application of mixed-effect modelling to clinical trials. Application of NONMEM to routine bioavailability data. An evaluation of population pharmacokinetics in therapeutic trials. Population pharmacokinetics of zidovudine. Standiford, and the Veterans Administration Cooperative Studies Group. Experiences in the application of NONMEM to pharmacokinetic data analysis. Population pharmacokinetics of quinidine. Netilmicin in the neonate: population pharmacokinetic analysis and dosing recommendations. Potential of population pharmacokinetics to reduce the frequency of blood sampling required for estimating kinetic parameters in neonates.
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Prospective data collection versus retrospective data assembly. Population pharmacokinetics of tobramycin. Modelling of individual pharmacokinetics for computer-aided drug dosage. Sheiner (eds.) NONMEM Users Guides, NONMEM Project Group, University of California, San Francisco, 1992.
NONMEM POPULATION PHARMACOKINETICS 2016 CALIFORNIA HOW TO
A suggestion on how to proceed when building population pharmacokinetic models is given. Thus, the choice of structural model can be affected as much by the covariate model as can the choice of covariate model be affected by the structural model the two choices are interestingly intertwined. For two real data sets, with which the two-compartment model was not selected preferentially, more complex covariate relationships were supported with the one-compartment model than with the two-compartment model. Only when the complexity of the one-compartment model was increased in terms of the covariate and statistical models was the difference in objective function values of the two structural models negligible. Initially, on the basis of a difference in the objective function values, the two-compartment model was selected over the one-compartment model. A simple categorical covariate acting on clearance was included. Data were simulated using a two-compartment model, but at late sample times, so that preferential selection of the two-compartment model should have been impossible. Simulations and data analysis were both performed with the program NONMEM. The influence of the choice of pharmacokinetic model on subsequent determination of covariate relationships in population pharmacokinetic analysis was studied using both simulated and real data sets.