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Modeling sleep data for a new drug in development using Markov mixed-effects models
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Division of Pharmacokinetics and Drug Therapy. (Farmakometri)ORCID iD: 0000-0003-3531-9452
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
2011 (English)In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 28, no 10, 2610-2627 p.Article in journal (Refereed) Published
Abstract [en]

To characterize the time-course of sleep in insomnia patients as well as placebo and concentration-effect relationships of two hypnotic compounds, PD 0200390 and zolpidem, using an accelerated model-building strategy based on mixed-effects Markov models. Data were obtained in a phase II study with the drugs. Sleep stages were recorded during eight hours of sleep for two nights per treatment for the five treatments. First-order Markov models were developed for one transition at a time in a sequential manner; first a baseline model, followed by placebo and lastly the drug models. To accelerate the process, predefined models were selected based on a priori knowledge of sleep, including inter-subject and inter-occasion variability. Baseline sleep was described using piece-wise linear models, depending on time of night and duration of sleep stage. Placebo affected light sleep stages; drugs also affected slow-wave sleep. Administering PD 0200390 30 min earlier than standard dosing was shown through simulations to reduce latency to persistent sleep by 40%. The proposed accelerated model-building strategy resulted in a model well describing sleep patterns of insomnia patients with and without treatments.

Place, publisher, year, edition, pages
2011. Vol. 28, no 10, 2610-2627 p.
Keyword [en]
Markov model, NONMEM, pharmacodynamics, polysomnography, population analysis, sleep, transition model, zolpidem
National Category
Medical and Health Sciences
URN: urn:nbn:se:uu:diva-97672DOI: 10.1007/s11095-011-0490-xISI: 000294811700023OAI: oai:DiVA.org:uu-97672DiVA: diva2:172701
Available from: 2008-10-30 Created: 2008-10-30 Last updated: 2015-08-14Bibliographically approved
In thesis
1. Methodological Studies on Models and Methods for Mixed-Effects Categorical Data Analysis
Open this publication in new window or tab >>Methodological Studies on Models and Methods for Mixed-Effects Categorical Data Analysis
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Effects of drugs are in clinical trials often measured on categorical scales. These measurements are increasingly being analyzed using mixed-effects logistic regression. However, the experience with such analyzes is limited and only a few models are used.

The aim of this thesis was to investigate the performance and improve the use of models and methods for mixed-effects categorical data analysis. 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. Two solutions are suggested; the Gaussian quadrature method and the back-step method. Two assumptions made with the proportional odds model have also been investigated. 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. 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 appropriateness of the likelihood ratio test was investigated for an analysis where dependence between observations is ignored, i.e. performing the analysis using the proportional odds model. 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. An alternative approach is to use a Markov model, in which dependence between observations is incorporated. 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.

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.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2008. 76 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 83
Pharmacodynamics, Categorical data, Markov model, Modelling, NONMEM, NLMIXED, Laplace, Gaussian quadrature, Back-Step Method, Proportional odds model, Differential odds model
National Category
Pharmaceutical Sciences
urn:nbn:se:uu:diva-9333 (URN)978-91-554-7316-7 (ISBN)
Public defence
2008-11-21, B42, BMC, Husargatan 3, Uppsala, 13:15
Available from: 2008-10-30 Created: 2008-10-30 Last updated: 2012-06-01Bibliographically approved

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Kjellsson, Maria C.Karlsson, Mats O.
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