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Bayesian hierarchical modeling of drug stability data

Authors

  • Jie Chen,

    Corresponding author
    1. Investigational Research, Merck Research Laboratories, P.O. Box 1000, UG1D-88, North Wales, PA 19454, U.S.A.
    • Investigational Research, Merck Research Laboratories, P.O. Box 1000, UG1D-88, North Wales, PA 19454, U.S.A.
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  • Jinglin Zhong,

    1. Division of Biometrics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002, U.S.A.
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  • Lei Nie

    1. Division of Biometrics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002, U.S.A.
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Abstract

Stability data are commonly analyzed using linear fixed or random effect model. The linear fixed effect model does not take into account the batch-to-batch variation, whereas the random effect model may suffer from the unreliable shelf-life estimates due to small sample size. Moreover, both methods do not utilize any prior information that might have been available. In this article, we propose a Bayesian hierarchical approach to modeling drug stability data. Under this hierarchical structure, we first use Bayes factor to test the poolability of batches. Given the decision on poolability of batches, we then estimate the shelf-life that applies to all batches. The approach is illustrated with two example data sets and its performance is compared in simulation studies with that of the commonly used frequentist methods. Copyright © 2008 John Wiley & Sons, Ltd.

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