Modelling a spontaneously reported side effect by use of a Markov mixed-effects model.
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
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.
IdentifiersURN: urn:nbn:se:uu:diva-75578DOI: 10.1007/s10928-005-0021-7PubMedID: 16283538OAI: oai:DiVA.org:uu-75578DiVA: diva2:103489