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Bayesian hierarchical modeling of drug stability data
Author(s) -
Chen Jie,
Zhong Jinglin,
Nie Lei
Publication year - 2008
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3220
Subject(s) - frequentist inference , computer science , bayesian probability , stability (learning theory) , random effects model , bayes' theorem , hierarchical database model , bayesian hierarchical modeling , sample size determination , statistics , econometrics , bayesian inference , data mining , machine learning , mathematics , artificial intelligence , medicine , meta analysis
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.