Rank-Based Procedures for Linear Models: Applications to Pharmaceutical Science Data

Abstract

Rank-based procedures for linear models generalize the simple Wilcoxon rank tests in the simple location models, inheriting their robustness and high efficiency properties. Given a general linear model, these rank-based procedures form a complete analysis, including estimation, confidence, and multiple comparison procedures, and tests of general linear hypotheses. In this article, these ranked-based procedures are reviewed in the context of pharmaceutical science data. Examples involving ANOVA- and ANCOVA-type designs are considered in some detail. We further present a Web-based interface incorporating the statistical software R and RGLM for the computation of these procedures. As discussed, the user need only visit our Web site to compute these procedures. Taken together these rank-based procedures offer the user an efficient and robust alternative to standard least squares procedures for linear models.