Stability data are often collected to determine the shelf-life of certain characteristics of a pharmaceutical product, for example, a drug's potency over time. Statistical approaches such as the linear regression models are considered as appropriate to analyze the stability data. However, most of these regression models in both theory and practice rely heavily on their underlying parametric assumptions, such as normality of the continuous characteristics or their transformations. In this article, we propose and study some rank-based regression procedures for the stability data when the linear regression models are semiparametric with unspecified error structure. Numerical studies including Monte Carlo simulations and practical examples are demonstrated with the proposed procedures as well.
Chen, Ying Qing; Pong, Annpey ; and Xing, Biao, "Rank Regression in Stability Analysis" (February 2003). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 127.