title: Rank regression in stability analysis.
abstract: 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 example are demonstrated with the proposed procedures as well.
authors.affiliation: Division of Biostatistics, School of Public Health, University of California, Berkeley, California 94720, USA. yqchen@stat.berkeley.edu
authors.forename: Ying Qing
authors.initials: YQ
authors.lastname: Chen
authors.forename: Annpey
authors.initials: A
authors.lastname: Pong
authors.forename: Biao
authors.initials: B
authors.lastname: Xing
mesh_terms: D004355:Drug Stability; D004356:Drug Storage / Q000592:standards / Q000706:statistics & numerical data; D016014:Linear Models; D013997:Time Factors