Markov chain Monte Carlo techniques for studying interoccasion and intersubject variability: Application to pharmacokinetic data

David J. Lunn, Leon J. Aarons

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Values of pharmacokinetic parameters may seem to vary randomly between dosing occasions. An accurate explanation of the pharmacokinetic behaviour of a particular drug within a population therefore requires two major sources of variability to be accounted for, namely interoccasion variability and intersubject variability. A hierarchical model that recognizes these two sources of variation has been developed. Standard Bayesian techniques were applied to this statistical model, and a mathematical algorithm based on a Gibbs sampling strategy was derived. The accuracy of this algorithm's determination of the interoccasion and intersubject variation in pharmacokinetic parameters was evaluated from various population analyses of several sets of simulated data. A comparison of results from these analyses with those obtained from parallel maximum likelihood analyses (NONMEM) showed that, for simple problems, the outputs from the two algorithms agreed well, whereas for more complex situations the NONMEM approach may be less accurate. Statistical analyses of a multioccasion data set of pharmacokinetic measurements on the drug metoprolol (the measurements being of concentrations of drug in blood plasma from human subjects) revealed substantial interoccasion variability for all structural model parameters. For some parameters, interoccasion variability appears to be the primary source of pharmacokinetic variation.
    Original languageEnglish
    Pages (from-to)73-91
    Number of pages18
    JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
    Volume46
    Issue number1
    DOIs
    Publication statusPublished - 1997

    Keywords

    • Gibbs sampling
    • Hierarchical models
    • Interoccasion variability
    • Intersubject variability
    • Pharmacokinetics
    • Population analysis

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