For the conditions tested, Gaussian quadrature performed without appreciable bias in parameter estimates. Also, simulations of new data based on estimated population parameters were performed to evaluate the usefulness of the estimated model. The model used for simulation was fitted to each data set for assessment of bias. The simulated response was a four-category variable on the ordinal scale with categories 0, 1, 2 and 3. This is a Monte Carlo simulation study where original data sets were derived from a known model and fixed study design. In particular, we have focused on situations with non-even distributions of the response categories and the impact of interpatient variability. The aim of this paper was to investigate the bias in parameter estimates, when models for ordered categorical data were estimated using methods employing different approximations of the likelihood integral the Laplacian approximation in NONMEM (without and with the centering option) and NLMIXED, and the Gaussian quadrature approximations in NLMIXED. The application of proportional odds models to ordered categorical data using the mixed-effects modeling approach has become more frequently reported within the pharmacokinetic/pharmacodynamic area during the last decade. 299-320 Article in journal (Refereed) Published Abstract Pharmaceutical Sciences Identifiers URN: urn:nbn:se:uu:diva-9333 ISBN: 978-9-7 (print) OAI: oai::uu-9333 DiVA, id: diva2:172702 Public defenceДў004 (English) In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. Pharmacodynamics, Categorical data, Markov model, Modelling, NONMEM, NLMIXED, Laplace, Gaussian quadrature, Back-Step Method, Proportional odds model, Differential odds model National Category 76 SeriesДigital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 83 Keywords Place, publisher, year, edition, pagesUppsala: Acta Universitatis Upsaliensis, 2008.

This thesis will hopefully contribute to a more confident use of models for categorical data analysis within the area of pharmacokinetic and pharmacodynamic modelling in the future. In the case of polychotomous data such model may involve considerable complexity and thus, a strategy for the reduction of the time-consuming model building with the Markov model and sleep data is presented. An alternative approach is to use a Markov model, in which dependence between observations is incorporated. The type I error was found to be affected thus assessing the actual critical value is prudent in order to verify the statistical significance level. performing the analysis using the proportional odds model. The appropriateness of the likelihood ratio test was investigated for an analysis where dependence between observations is ignored, i.e. An alternative model, the differential odds model, was developed and shown to be an improvement, in regard to statistical significance as well as predictive performance, over the proportional odds model for such data. The assumption with proportional odds for all categories was shown to be unsuitable for analysis of data arising from a ranking scale of effects with several underlying causes. Two assumptions made with the proportional odds model have also been investigated. Two solutions are suggested the Gaussian quadrature method and the back-step method. The Laplacian method was shown to produce biased parameter estimates if (i) the data variability is large or (ii) the distribution of the responses is skewed. The aim of this thesis was to investigate the performance and improve the use of models and methods for mixed-effects categorical data analysis. However, the experience with such analyzes is limited and only a few models are used. These measurements are increasingly being analyzed using mixed-effects logistic regression. 2008 (English) Doctoral thesis, comprehensive summary (Other academic) Abstract Д®ffects of drugs are in clinical trials often measured on categorical scales.

Proportional error nonmem