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 classified into different body systems via the stratified 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.



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