Nonparametric equivalence testing with respect to the median difference.
The problem of comparing two independent groups of univariate data in the sense of testing for equivalence is considered for a fully nonparametric setting. The distribution of the data within each group may be a mixture of both a continuous and a discrete component, and no assumptions are made regarding the way in which the distributions of the two groups of data may differ from each other - in particular, the assumption of a shift model is avoided. The proposed equivalence testing procedure for this scenario refers to the median of the independent difference distribution, i.e. to the median of the differences between independent observations from the test group and the reference group, respectively. The procedure provides an asymptotic equivalence test, which is symmetric with respect to the roles of 'test' and 'reference'. It can be described either as a two-one-sided-tests (TOST) approach, or equivalently as a confidence interval inclusion rule. A one-sided variant of the approach can be applied analogously to non-inferiority testing problems. The procedure may be generalised to equivalence testing with respect to quantiles other than the median, and is closely related to tolerance interval type inference.
Meier
Ulrich
U
Department of Medical Data Services, Boehringer Ingelheim Pharma GmbH & Co. KG, Ingelheim am Rhein, Germany. meieru@ing.boehringer-ingelheim.com
D003627:Data Interpretation, Statistical; D005069:Evaluation Studies as Topic; D012107:Research Design / Q000706:statistics & numerical data*; D018709:Statistics, Nonparametric