Equivalence testing for similarity in bioassays using bioequivalence criteria on the relative bioactivity

Similarity in bioassays means that the test preparation behaves as a dilution of the standard preparation with respect to their biological effect. Thus, similarity must be investigated to confirm this biological property. Historically, this was typically conducted with traditional hypothesis testing, but this has received substantial criticism. Failing to reject similarity does not imply that the 2 preparations are similar. Also, rejecting similarity when bioassay variability is small might simply demonstrate a nonrelevant deviation in similarity. To remedy these concerns, equivalence testing has been proposed as an alternative to traditional hypothesis testing, and it has found its way in the official guidelines. However, similarity has been discussed mainly in terms of the parameters in the dose‐response curves of the standard and test preparations, but the consequences of nonsimilarity on the relative bioactivity have never been investigated. This article provides a general equivalence approach to evaluate similarity that is directly related to bioequivalence on the relative bioactivity of the standard and test preparations. Bioequivalence on the relative bioactivity can only be guaranteed for positive (only nonblanks) and finite dose intervals. The approach is demonstrated on 4 case studies in which we also show how to calculate a sample size and how to investigate the power of equivalence on similarity.     

A comparison of statistical methods for combining relative bioactivities from parallel line bioassays

This paper compares the ordinary unweighted average, weighted average, and maximum likelihood methods for estimating a common bioactivity from multiple parallel line bioassays. Some of these or similar methods are also used in meta‐analysis. Based on a simulation study, these methods are assessed by comparing coverage probabilities of the true relative bioactivity and the length of the confidence intervals computed for these methods. The ordinary unweighted average method outperforms all statistical methods by consistently giving the best coverage probability but with somewhat wider confidence intervals. The weighted average methods give good coverage and smaller confidence intervals when combining homogeneous bioactivities. For heterogeneous bioactivities, these methods work well when a liberal significance level for testing homogeneity of bioactivities is used. The maximum likelihood methods gave good coverage when homogeneous bioactivities were considered. Overall, the preferred methods are the ordinary unweighted average and two weighted average methods that were specifically developed for bioassays. Copyright © 2013 John Wiley & Sons, Ltd.