A new linear model-based approach for modeling the area under the curve
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Outcome versus time data is commonly encountered in biomedical and clinical research. A common strategy adopted in analyzing such longitudinal data is to condense the repeated measurements on each individual into a single summary statistic such as the area under the curve (AUC). Standard parametric or non-parametric methods are then applied to perform inferences on the conditional AUC distribution. Disadvantages of this approach include the disregard of the within-subject variation in the longitudinal profile. We propose a general linear model approach, accounting for the within-subject variance, for estimation and hypothesis tests about the mean areas. Inferential properties of our approach are compared to those from standard methods of analysis using Monte Carlo simulation studies. The impact of missing data, within-subject heterogeneity and homogeneity of variance are also evaluated. Real working examples are used to illustrate our approach. It is seen that the proposed approach is associated with a significant power advantage over traditional methods, especially when missing data is encountered. This approach is extended to the assessment of bioequivalence and its properties are investigated when observations fall below the limit of detection.