In Vitro diagnostic (IVD) reagent stability is typically evaluated using regression analysis of measurand drift across time following CLSI guideline EP25-A. The corresponding stability duration establishment has several limitations. The stability duration conclusion is based on a two-stage acceptance criteria using the p-value of the regression slope followed by the 95% confidence interval (CI) on the fitted regression line if the p-value < 0.05. This analysis technique is based on traditional statistical hypothesis testing; however, the statistical equivalence testing framework better represents the goals of IVD reagent stability evaluation. The resulting stability duration CI does not achieve 95% coverage probability and the statistical properties of the estimated stability duration are substantially impacted by presence of common variance components not accounted for during power analysis and by not accounting for variability in the baseline estimate. The current proposal based on the equivalence testing framework uses a one-stage acceptance criteria based on the 95% CI for proportional measurand drift derived from the regression fit. The proposed methodology was applied to automated immunoassay data (Akbas et al., 2016). Monte Carlo simulation studies are presented to illustrate the improved statistical properties of the current proposal along with an example power analysis for study design.

In Vitro diagnostic (IVD) reagent stability is typically evaluated using regression analysis of measurand drift across time following CLSI guideline EP25-A. The corresponding stability duration establishment has several limitations. The stability duration conclusion is based on a two-stage acceptance criteria using the p-value of the regression slope followed by the 95% confidence interval (CI) on the fitted regression line if the p-value < 0.05. This analysis technique is based on traditional statistical hypothesis testing; however, the statistical equivalence testing framework better represents the goals of IVD reagent stability evaluation. The resulting stability duration CI does not achieve 95% coverage probability and the statistical properties of the estimated stability duration are substantially impacted by presence of common variance components not accounted for during power analysis and by not accounting for variability in the baseline estimate. The current proposal based on the equivalence testing framework uses a one-stage acceptance criteria based on the 95% CI for proportional measurand drift derived from the regression fit. The proposed methodology was applied to automated immunoassay data (Akbas et al., 2016). Monte Carlo simulation studies are presented to illustrate the improved statistical properties of the current proposal along with an example power analysis for study design.