  Multivariate equivalence testing is needed in a variety of scenarios for drug
development. For example, drug products obtained from natural sources may
contain many components for which the individual effects and/or their
interactions on clinical efficacy and safety cannot be completely
characterized. Such lack of sufficient characterization poses a challenge for
both generic drug developers to demonstrate and regulatory authorities to
determine the sameness of a proposed generic product to its reference product.
Another case is to ensure batch-to-batch consistency of naturally derived
products containing a vast number of components, such as botanical products.
The equivalence or sameness between products containing many components that
cannot be individually evaluated needs to be studied in a holistic manner.
Multivariate equivalence test based on Mahalanobis distance may be suitable to
evaluate many variables holistically. Existing studies based on such method
assumed either a predetermined constant margin, for which a consensus is
difficult to achieve, or a margin derived from the data, where, however, the
randomness is ignored during the testing. In this study, we propose a
multivariate equivalence test based on Mahalanobis distance with a data-drive
margin with the randomness in the margin considered. Several possible
implementations are compared with existing approaches via extensive simulation
studies.
