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Although the other components can be controlled through clinical trial design, variability is dictated by subject characteristics. Variability, especially within-subject variability (WSV), is the most important factor to determine sample size. Target sample sizes in clinical trials are calculated based on various components, including type I error, type II error, significance level, power, and variability. In conclusion, the estimated RV accurately predicted WSV in single-period studies, validating this method for sample size estimation in clinical trials.
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Using the eperisone dataset, RV was 44% to 48%, close to the true value of 50%. RV was underestimated at WSV of 50% or greater, even with datasets having low IIV and numerous subjects. With WSV of 40% or less, regardless of IIV magnitude, RV was well approximated by WSV for sample sizes greater than 18 subjects. In addition, 3 × 3 bioequivalence results of eperisone were used to evaluate method performance with a real clinical dataset. The estimated residual variability (RV) resulting from population pharmacokinetic methods were compared with WSV values. We simulated 1000 virtual pharmacokinetic clinical trial datasets based on single-period and dense sampling studies, with various study sizes and levels of WSV and interindividual variabilities (IIVs). We have developed an efficient population-based method to predict WSV accurately with single-period clinical trial data and demonstrate method performance with eperisone. However, it is difficult to determine WSV without replicate-designed clinical trial data, and statisticians typically estimate optimal sample sizes using total variability, not WSV. Sample sizes for single-period clinical trials, including pharmacokinetic studies, are statistically determined by within-subject variability (WSV).