Methods to control the empirical type I error rate in average bioequivalence tests for highly variable drugs.

Average bioequivalence tests are used in clinical trials to determine whether a generic drug has the same effect as an original drug in the population. For highly variable drugs whose intra-subject variances of direct drug effects are high, extra criteria are needed in bioequivalence studies. Currently used average bioequivalence tests for highly variable drugs recommended by the European Medicines Agency and the US Food and Drug Administration use sample estimators in the null hypotheses of interest. They cannot control the empirical type I error rate, so the consumer's risk is higher than the predetermined level. In this paper, we propose two new statistically sound methods that can control the empirical type I error rate without involving any sample estimators in the null hypotheses. In the proposed methods, we consider the average level of direct drug effects and the intra-subject variance of the direct drug effects. The first proposed method tests the latter parameter first to determine whether a product should be regarded as a highly variable drug, and then tests the former using corresponding bioequivalence limits. The second proposed method tests these two parameters simultaneously to capture the bioequivalence region. Extensive simulations are done to compare these methods. The simulation results show that the proposed methods have good performance on controlling the empirical type I error rate. The proposed methods are useful for pharmaceutical manufacturers and regulators.