Sample size determination in bioequivalence studies using statistical assurance. Issue 10 (13th August 2019)

• Record Type: Journal Article
• Authors: Ring, A.; Lang, B.; Kazaroho, C.; Labes, D.; Schall, R.; Schütz, H.
• Is Part Of: British journal of clinical pharmacology. Volume 85:Issue 10(2019)
• Page Start: 2369
• Page End: 2377
• Publication Date: 2019-08-13
• Subjects: bioequivalence -- crossover trial -- sample size determination -- statistical power -- trial design; Pharmacology -- Periodicals; Drugs -- Periodicals; 615.1
• DOI: 10.1111/bcp.14055
• Languages: English
• ISSNs: 0306-5251

Abstract

Abstract : 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.