Detlew Labes

Comparative bioavailability studies often involve multiple groups of subjects for a variety of reasons, such as clinical capacity limitations. This raises questions about the validity of pooling data from these groups in the statistical analysis and whether a group-by-treatment interaction should be evaluated. We investigated the presence or absence of group-by-treatment interactions through both simulation techniques and a meta-study of well-controlled trials. Our findings reveal that the test falsely detects an interaction when no true group-by-treatment interaction exists. Conversely, when a true group-by-treatment interaction does exist, it often goes undetected. In our meta-study, the detected group-by-treatment interactions were observed at approximately the level of the test and, thus, can be considered false positives. Testing for a group-by-treatment interaction is both misleading and uninformative. It often falsely identifies an interaction when none exists and fails to detect a real one. This occurs because the test is performed between subjects in crossover designs, and studies are powered to compare treatments within subjects. This work demonstrates a lack of utility for including a group-by-treatment interaction in the model when assessing single-site comparative bioavailability studies, and the clinical trial study structure is divided into groups. Graphical Abstract

Purpose: More than a decade ago the option to assess highly variable drugs / drug products by reference-scaled average bioequivalence was introduced in regulatory practice. Recommended approaches differ between jurisdictions and may lead to different conclusions even for the same data set. According to our knowledge, implemented methods have not been directly compared for their operating characteristics (Type I Error and power). Methods: We performed Monte Carlo simulations to assess the consumer risk and the clinically relevant difference for the recommended regulatory settings. Results: In all methods for reference-scaled average bioequivalence the Type I Error can be inflated with a consequently compromised consumer risk. Furthermore, the clinically relevant difference could vary between studies performed with the same reference product. Conclusions: Only average bioequivalence with fixed – widened – limits would both maintain the consumer risk and offer an unambiguously defined clinically not relevant difference. As long as such an approach is not implemented in regulatory practice, we recommend adjusting the level of the test a.

SUMMARY: For the clinical development of a new drug, the determination of dose‐proportionality is an essential part of the pharmacokinetic evaluations, which may provide early indications of non‐linear pharmacokinetics and may help to identify sub‐populations with divergent clearances. Prior to making any conclusions regarding dose‐proportionality, the goodness‐of‐fit of the model must be assessed to evaluate the model performance. We propose the use of simulation‐based visual predictive checks to improve the validity of dose‐proportionality conclusions for complex designs. We provide an illustrative example and include a table to facilitate review by regulatory authorities.

In order to help companies qualify and validate the software used to evaluate bioequivalence trials in a replicate design intended for average bioequivalen

Aims: Bioequivalence (BE) trials aim to demonstrate that the 90% confidence interval of the T/R‐ratio of the pharmacokinetic metrics between two formulations (test [T] and reference [R]) of a drug is fully included in the acceptance interval [0.80, 1.25]. Traditionally, the sample size of BE trials is based on a power calculation based on the intrasubject variability coefficient of variation (CV) and the T/R‐ratio of the metrics. Since the exact value of the T/R‐ratio is not known prior to the trial, it is often assumed that the difference between the treatments does not exceed 5%. Hence, uncertainty about the T/R‐ratio is expressed by using a fixed value for the sample size calculation. We propose to characterise the uncertainty about the T/R‐ratio by a (normal) distribution for the log(T/R‐ratio), with an assumed mean of log θ = 0.00 (i.e. θ = 1.00) and a standard deviation σ_u, which quantifies the uncertainty. Evaluating this distribution leads to the statistical assurance of the BE trial.Methods: The assurance of a clinical trial can be derived by integrating the power over the distribution of the input parameters, in this case, the assumed distribution of the log(T/R)‐ratio. Because it is an average power, the assurance can be interpreted as a measure of the probability of success that does not depend on a specific assumed value for the log(T/R)‐ratio. The relationship between power and assurance will be analysed by comparing the numerical outcomes.Results: Using the assurance concept, values of the standard deviation for the distribution of potential log(T/R)‐ratios can be chosen to reflect the magnitude of uncertainty. For most practical cases (i.e. when 0.95 ≤ θ ≤ 1.05), the sample size is not, or only slightly, changed when σ = |log(θ)|.Conclusion: The advantage of deriving the assurance for BE trials is that uncertainty is directly expressed as a parameter of variability.

Purpose To verify previously reported findings for the European Medicines Agency’s method for Average Bioequivalence with Expanding Limits (ABEL) for assessing highly variable drugs and to extend the assessment for other replicate designs in a wide range of sample sizes and CVs. To explore the properties of a new modified method which maintains the consumer risk ≤0.05 in all cases. Methods Monte-Carlo simulations of three different replicate designs covering a wide range of sample sizes and intra-subject variabilities were performed. Results At the switching variability of CV _wR 30% the consumer risk is substantially inflated to up to 9.2%, which translates into a relative increase of up to 84%. The critical region of inflated type I errors ranges approximately from CV _wR 25 up to 45%. The proposed method of iteratively adjusting α maintains the consumer risk at the desired level of ≤5% independent from design, variability, and sample size. Conclusions Applying the European Medicines Agency’s ABEL method at the nominal level of 0.05 inflates the type I error to an unacceptable degree, especially close to a CV _wR of 30%. To control the type I error nominal levels ≤0.05 should be employed. Iteratively adjusting α is suggested to find optimal levels of the test.

In order to help companies qualify and validate the software used to evaluate bioequivalence trials with two parallel treatment groups, this work aims to define datasets with known results. This paper puts a total 11 datasets into the public domain along with proposed consensus obtained via evaluations from six different software packages (R, SAS, WinNonlin, OpenOffice Calc, Kinetica, EquivTest). Insofar as possible, datasets were evaluated with and without the assumption of equal variances for the construction of a 90% confidence interval. Not all software packages provide functionality for the assumption of unequal variances (EquivTest, Kinetica), and not all packages can handle datasets with more than 1000 subjects per group (WinNonlin). Where results could be obtained across all packages, one showed questionable results when datasets contained unequal group sizes (Kinetica). A proposal is made for the results that should be used as validation targets.