A nonparametric test for similarity of marginals—With applications to the assessment of population bioequivalence

Tests for multivariate normality—a critical review with emphasis on weighted $$L^2$$-statistics

This article gives a synopsis on new developments in affine invariant tests for multivariate normality in an i.i.d.-setting, with special emphasis on asymptotic properties of several classes of weighted $$L^2$$ L 2 -statistics. Since weighted $$L^2$$ L 2 -statistics typically have limit normal distributions under fixed alternatives to normality, they open ground for a neighborhood of model validation for normality. The paper also reviews several other invariant tests for this problem, notably the energy test, and it presents the results of a large-scale simulation study. All tests under study are implemented in the accompanying -package .

Bioequivalence testing by statistical shape analysis

Bioequivalence testing has been traditionally centered in summary variables such as AUC, C (max) and t (max) which filter out the intrinsic information conveyed by discrete sequential concentration-time observations. Comparing entire concentration-time profiles between test and reference formulations for bioequivalence purposes provides stronger evidence about either their similarity or their discrepancy. The Kullback-Leibler information criterion (KLIC) may be computed for each concentration-time across all subjects between formulations of the same drug, with a standard crossover study design. It has been shown that if properly scaled it follow a chi-squared distribution and dependent p-values may be computed in order to construct a bioequivalence criterion. Extensive simulations and real data were used to compare it with the current standard procedures. This statistical shape analysis method may provide important clinical and regulatory advantages.