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Modelling a spontaneously reported side effect by use of a Markov mixed-effects model.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
2005 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 32, no 2, 261-281 p.Article in journal (Refereed) Published
Abstract [en]

Aims: To present a method for analyzing side-effect data where change in severity is spontaneouslyreported during the experiment. Methods: A clinical study in 12 healthy volunteers aimed toinvestigate the concentration-response characteristics of a CNS-specific side-effect was conducted.After an open session where the subjects experienced the side-effect and where the individualpharmacokinetic parameters were evaluated they were randomized to a sequence of three differentinfusion rates of the drug in a double-blinded crossover way. The infusion rates were individualizedto achieve the same target concentration in all subjects and different drug input rates wereselected to mimic absorption profiles from different formulations. The occurrence of the specificside-effect and any subsequent change in severity was self-reported by the subjects. Severity wasrecorded as 0 = no side-effect, 1 = mild side-effect and 2 = moderate or severe side-effect.Results: The side-effect data were analyzed using a mixed-effects model for ordered categoricaldata with and without Markov elements. The former model estimated the probability of having acertain side-effect score conditioned on the preceding observation and drug exposure. The observednumbers of transitions between scores were from 0 ->1: 24, from 0 ->2: 11, from 1 ->2: 23, from2 ->1: 1, from 2 ->0: 32 and from 1 ->0: 2. The side-effect model consisted of an effect-compartmentmodel with a tolerance compartment. The predictive performance of the Markov model wasinvestigated by a posterior predictive check (PPC), where 100 datasets were simulated from thefinal model. Average number of the different transitions from the PPC was from 0 ->1: 26, from0 ->2: 11, from 1 ->2: 25, from 2 ->1: 1, from 2 ->0: 35 and from 1 ->0: 1. A similar PPCfor the model without Markov elements was at considerable disparity with the data. Conclusion:This approach of incorporating Markov elements in an analysis of spontaneously reported categoricalside-effect data could adequately predict the observed side-effect time course and could beconsidered in analyses of categorical data where dependence between observations is an issue.

Place, publisher, year, edition, pages
2005. Vol. 32, no 2, 261-281 p.
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-75578DOI: 10.1007/s10928-005-0021-7PubMedID: 16283538OAI: oai:DiVA.org:uu-75578DiVA: diva2:103489
Available from: 2006-03-08 Created: 2006-03-08 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Models for Ordered Categorical Pharmacodynamic Data
Open this publication in new window or tab >>Models for Ordered Categorical Pharmacodynamic Data
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In drug development clinical trials are designed to investigate whether a new treatment is safe and has the desired effect on the disease in the target patient population. Categorical endpoints, for example different ranking scales or grading of adverse events, are commonly used to measure effects in the trials.

Pharmacokinetic/Pharmacodynamic (PK/PD) models are used to describe the plasma concentration of a drug over time and its relationship to the effect studied. The models are utilized both in drug development and in discussions with drug regulating authorities. Methods for incorporation of ordered categorical data in PK/PD models were studied using a non-linear mixed effects modelling approach as implemented in the software NONMEM. The traditionally used proportional odds model was used for analysis of a 6-grade sedation scale in acute stroke patients and for analysis of a T-cell receptor expression in patients with Multiple Sclerosis, where the results also were compared with an analysis of the data on a continuous scale. Modifications of the proportional odds model were developed to enable analysis of a spontaneously reported side-effect and to analyze situations where the scale used is heterogeneous or where the drug affects the different scores in the scale in a non-proportional way. The new models were compared with the proportional odds model and were shown to give better predictive performances in the analyzed situations.

The results in this thesis show that categorical data obtained in clinical trials with different design and different categorical endpoints successfully can be incorporated in PK/PD models. The models developed can also be applied to analyses of other ordered categorical scales than those presented.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2005. 60 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 20
Keyword
Pharmacokinetics/Pharmacotherapy, Pharmacodynamic, Modelling, Categorical data, NONMEM, Proportional odds model, Markov model, Differential drug effect model, Farmakokinetik/Farmakoterapi
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:uu:diva-6125 (URN)91-554-6394-0 (ISBN)
Public defence
2005-12-02, B42, BMC, Husargatan 3, Uppsala, 09:15
Opponent
Supervisors
Available from: 2005-11-11 Created: 2005-11-11 Last updated: 2011-07-08Bibliographically approved

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Kågedal, MattsKarlsson, Mats O

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