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Related Experiment Videos

Evaluating pharmacokinetic/pharmacodynamic models using the posterior predictive check.

Y Yano1, S L Beal, L B Sheiner

  • 1Department of Biopharmaceutical Sciences, School of Pharmacy, University of California, San Francisco, San Francisco, California, USA.

Journal of Pharmacokinetics and Pharmacodynamics
|May 31, 2001
PubMed
Summary

The posterior predictive check (PPC) is a conservative model evaluation tool for pharmacokinetic (PK) and pharmacodynamic (PD) analysis, rarely invalidating useful models. Its power is limited, especially for statistics dependent on model parameters.

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Area of Science:

  • Pharmacometrics
  • Statistical Modeling
  • Model Evaluation

Background:

  • The posterior predictive check (PPC) is a key tool for evaluating statistical models.
  • Its application in pharmacokinetic (PK) and pharmacodynamic (PD) modeling requires careful examination.
  • Understanding PPC performance is crucial for reliable model assessment in drug development.

Purpose of the Study:

  • To investigate the properties of the posterior predictive check (PPC) for pharmacokinetic (PK) and pharmacodynamic (PD) model evaluation.
  • To assess the type-I error rate and statistical power of the PPC under various simulation and analysis scenarios.
  • To determine the influence of different statistics and posterior distribution approximations on PPC performance.

Main Methods:

  • Simulated extensive sampling data from single individuals using simple PK/PD and error models.

Related Experiment Videos

  • Applied PPC to analyze models, comparing them against simulation models (null vs. alternative hypotheses).
  • Evaluated five specific PK/PD models (mono- and biexponential PK, Emax and sigmoid Emax PD) with different error structures.

    • Main Results:

    • The PPC demonstrated conservatism under the null hypothesis, rarely invalidating correct models.
    • Statistical power was generally low, particularly for statistics that were functions of both data and parameters.
    • No significant advantage was observed for different methods of approximating the posterior distribution on model parameters.

    Conclusions:

    • The PPC is a reliable tool that tends to be conservative, minimizing incorrect model rejections.
    • Enhancing the power of PPC requires careful selection of statistics, especially those not heavily reliant on parameter estimates.
    • The choice of posterior approximation method did not substantially impact PPC performance in this study.