title: A Novel Approach to Testing for Average Bioequivalence Based on Modeling the Within-Period Dependence Structure.
abstract: Bioequivalence trials are commonly conducted to assess therapeutic equivalence between a generic and an innovator brand formulations. In such trials, drug concentrations are obtained repeatedly over time and are summarized using a metric such as the area under the concentration vs. time curve (AUC) for each subject. The usual practice is to then conduct two one-sided tests using these areas to evaluate for average bioequivalence. A major disadvantage of this approach is the loss of information encountered when ignoring the correlation structure between repeated measurements in the computation of areas. In this article, we propose a general linear model approach that incorporates the within-subject covariance structure for making inferences on mean areas. The model-based method can be seen to arise naturally from the reparameterization of the AUC as a linear combination of outcome means. We investigate and compare the inferential properties of our proposed method with the traditional two one-sided tests approach using Monte Carlo simulation studies. We also examine the properties of the method in the event of missing data. Simulations show that the proposed approach is a cost-effective, viable alternative to the traditional method with superior inferential properties. Inferential advantages are particularly apparent in the presence of missing data. To illustrate our approach, a real working example from an asthma study is utilized.
authors: a Department of Biostatistics , Vanderbilt University School of Medicine , Nashville , Tennessee , USA.
authors: Rameela
authors: R
authors: Chandrasekhar
authors: b Department of Biostatistics , University at Buffalo , Buffalo , New York , USA.
authors: Yi
authors: Y
authors: Shi
authors: b Department of Biostatistics , University at Buffalo , Buffalo , New York , USA.
authors: Alan D
authors: AD
authors: Hutson
authors: b Department of Biostatistics , University at Buffalo , Buffalo , New York , USA.
authors: Gregory E
authors: GE
authors: Wilding
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D018927:Anti-Asthmatic Agents / Q000627:therapeutic use; D019540:Area Under Curve; D001249:Asthma / Q000188:drug therapy / Q000503:physiopathology; D002648:Child; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003198:Computer Simulation; D016001:Confidence Intervals; D003362:Cost-Benefit Analysis; D018592:Cross-Over Studies; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D009010:Monte Carlo Method; D013810:Therapeutic Equivalency
chemical_list: D018927:Anti-Asthmatic Agents
keywords: Area under the curve; Bioequivalence; Longitudinal data; Trapezoidal rule.