Dataset for: Using Accelerated Drug Stability Results to Inform Long-Term Studies in Shelf-Life Determination

In the pharmaceutical industry, the shelf-life of a drug product is determined by data gathered from stability studies and is intended to provide consumers with a high degree of confidence that the drug retains its strength, quality, and purity under appropriate storage conditions. In this paper we focus on liquid drug formulations and propose a Bayesian approach to estimate a drug product's shelf-life, where prior knowledge gained from the accelerated study conducted during the drug development stage is used to inform the long-term study. Classical and non-linear Arrhenius regression models are considered for the accelerated conditions, and two examples are given where posterior results from the accelerated study are used to construct priors for a long-term stability study.