Analysis of Adverse Events in Drug Safety: A Multivariate Approach Using Quasi-Least Squares

Hanjoo Kim, University of Pennsylvania School of Medicine
Justine Shults, Univeristy of Pennsylvania Department of Biostatistics
Scott Patterson, Wyeth Research
Robert Goldberg-Alberts, Wyeth Research


Safety assessment in drug development involves numerous statistical challenges, and yet statistical methodologies and their applications to safety data have not been fully developed, despite a recent increase of interest in this area. In practice, a conventional univariate approach for analysis of safety data involves application of the Fisher's exact test to compare the proportion of subjects who experience adverse events (AEs) between treatment groups; This approach ignores several common features of safety data, including the presence of multiple endpoints, longitudinal follow-up, and a possible relationship between the AEs within body systems. In this article, we propose various regression modeling strategies to model multiple longitudinal AEs that are biologically classi ed into di erent body systems via the strati ed quasi-least squares (SQLS) method. We then analyze safety data from a clinical drug development program at Wyeth Research that compared an experimental drug with a standard treatment using SQLS, which could be a superior alternative to application of the Fisher's exact test.