Evaluation of type I error rates when modeling ordered categorical data in NONMEM

Journal of Pharmacokinetics and Pharmacodynamics
Ulrika WählbyE N Jonsson

Abstract

The development of non-linear mixed pharmacokinetic/pharmacodynamic models for continuous variables is usually guided by graphical assessment of goodness of fit and statistical significance criteria. The latter is usually the likelihood ratio test (LR). When the variable to be modeled is categorical, on the other hand, the available graphical methods are less informative and/or more complicated to use and the modeler needs to rely more heavily on statistical significance assessment in the model development. The aim of this study was to evaluate the type I error rates, obtained from using the LR test, for inclusion of a false parameter in a non-linear mixed effects model for ordered categorical data when modeling with NONMEM. Data with four ordinal categories were simulated from a logistic model. Two nested multinomial models were fitted to the data, the model used for simulation and a model containing one additional parameter. The difference in fit (objective function value) between models was calculated. Three types of models were explored; (i) a model without interindividual variability (IIV) where the addition of a parameter describing IIV was assessed, (ii) a model with IIV where the addition of a drug effect parameter (eit...Continue Reading

Citations

Oct 23, 2008·Cancer Chemotherapy and Pharmacology·Ricardo Nalda-MolinaJuan Jose Perez-Ruixo
Dec 8, 2009·Journal of Pharmacokinetics and Pharmacodynamics·Kayode Ogungbenro, Leon Aarons
Jun 11, 2011·Journal of Pharmacokinetics and Pharmacodynamics·Kayode Ogungbenro, Leon Aarons
Dec 14, 2011·Journal of Pharmacokinetics and Pharmacodynamics·Chakradhar V LagishettyStephen B Duffull
Jul 28, 2013·CPT: Pharmacometrics & Systems Pharmacology·K E KarlssonM O Karlsson
Jan 28, 2010·Basic & Clinical Pharmacology & Toxicology·Leon Aarons, Kayode Ogungbenro

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