A parametric approach to population bioequivalence

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.

A multivariate test for population bioequivalence

In this article, we propose a multivariate generalization of the criteria for testing univariate population bioequivalence. Recently, a number of approaches for testing multivariate equivalence have appeared in the literature. Most of them consider a multivariate equivalence region, which implies simultaneous comparison of means in each dimension. In contrast, our proposal combines a comparison of means and a comparison of variances into a single aggregate criterion, using the trace of the covariance matrix as a scalar measure of the total variability. We use a confidence interval approach to multivariate population bioequivalence testing, similar to the univariate case. Two versions of the modified large-sample confidence interval for the linearized multivariate criterion are constructed. In a simulation study, we evaluate the empirical coverage of these confidence intervals and rejection rates of the corresponding tests in finite samples. The proposed methodology is illustrated with an example of testing equivalence of the spray pattern of nasal sprays.

Generalized treatment effects for clinical trials

Practice in the analysis of clinical trials with continuously measured endpoints is to focus on the difference or percentage change in mean or median response. However, treatments may have effects on the distribution of responses other than on the average response. We sought an approach to such generalized treatment effects that: (i) targets a parameter that is easily understood by our clinical colleagues; and (ii) employs confidence intervals as the basis for inference. We consider one such approach based on work in reliability theory, namely setting Pr[Y>X] as the target parameter, and compare this approach to an earlier one due to O'Brien. The two approaches have similar properties when they both seek to reject the null hypothesis of no effect due to different variances but differ when the larger variance corresponds to the larger mean. In that case, our approach views that the larger variance attenuates the effect of the larger mean. Out suggested approach applies easily to positive control (clinical) equivalence trials.

Individual bioequivalence--has its time come?

A forthcoming draft Guidance of the Food and Drug Administration is expected to replace the current framework for the assessment of bioequivalence, based on the comparison of average kinetic responses, with a very different approach, the evaluation of individual bioequivalence. It emphasizes the estimation of intraindividual variances of the contrasted drug products as well as of the subject-formulation interaction, and requires the conduct of 4- (or 3-) period crossover bioequivalence trials. In order to facilitate the discussion of the draft Guidance and of the new approach, the underlying rationale, principles and procedures are described. Following this, various open questions are raised. It is suggested that their resolution should be carefully and widely discussed, and that more research and experience is needed before the possible implementation of the new approach.