Exploratory data analysis for outlier detection in bioequivalence studies

Exploratory Data Analysis techniques are recognized as useful tools in outlier detection through visual representations. One limitation of this direction is the lack of studies concerning the reliability of the visual interpretation. In this paper we propose a method that combines an Exploratory Data Analysis technique, Andrews curves, with a statistical approach which can be applied to automatically classify the data. Using a simulation study we show that the results provided by the Andrews curves approach are markedly superior to the estimates distance test (the best proposed method for detecting outliers revealed in the literature) for the crossover bioequivalence design.