A Ring1,2, B Lang3, C Kazaroho4, D Labes5, R Schall1,6, H Schütz7. 1. University of the Free State, Bloemfontein, South Africa. 2. medac, Wedel, Germany. 3. Boehringer Ingelheim, Biberach, Germany. 4. AIMS Rwanda, Kigali, Rwanda. 5. Consultant, Berlin, Germany. 6. IQVIA Biostatistics, Bloemfontein, South Africa. 7. BEBAC, Vienna, Austria.
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 σu , 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.
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 σu , 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.
Authors: Alejandro Muggeri; Miguel Vago; Sebastián Pérez; Marcelo Rubio; Cecilia González; Cristian Magariños; Mónica Rosenberg; Fernando Costa; Santiago Pérez-Lloret Journal: Drugs R D Date: 2017-09