title: Bayesian hierarchical modeling of drug stability data.
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.
authors.affiliation: Investigational Research, Merck Research Laboratories, North Wales, PA 19454, USA. jie_chen@merck.com
authors.forename: Jie
authors.initials: J
authors.lastname: Chen
authors.forename: Jinglin
authors.initials: J
authors.lastname: Zhong
authors.forename: Lei
authors.initials: L
authors.lastname: Nie
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004355:Drug Stability; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method