#Paired t test jmp verification#
#Paired t test jmp trial#

This is inconvenient when the goal is to show comparability between two or more groups.Īn approach widely used in clinical trial statistics and which is gaining popularity in pharmaceutical and biotech settings, the TOST is a method for declaring the comparability of equivalence that is built around comparing two or more group means and their respective mean difference confidence intervals against predetermined equivalence limits. One can only reject the null hypothesis or show that the groups are different using the common t-test. The opposite cannot be declared, however, when no statistically significant difference is observed. In practice, given sufficient sample size, even differences that are too small to be meaningful may be declared statistically significant. The effect of declaring the difference statistically significant indicates that the null hypothesis is rejected the groups represent two or more different distributions of values and are in fact not equal. The test statement or the null hypothesis is that the groups are not different. When comparing two or more groups of data, the more common approach is to determine if the difference in group means (a group mean represents the average of all data within the group) is sufficiently large to be declared statistically significant. 8–10 This study applies TOST to compare the cleanability of protein drug products. This method is well accepted by the FDA and is widely used in the industry. The two-one-sided t-test (TOST) is a commonly used statistical tool for comparability purposes, especially for method transfers between two laboratories, when the goal is to demonstrate equivalency between the receiving and transferring laboratory. After multiple cleaning time data points were generated for each product, a robust statistical method was needed to adequately assess the comparability of these cleaning time distributions. Because of the variability observed in the cleaning times, data points were collected in replicates and the statistical error was estimated. In this study, we apply the bench-scale model to evaluate the relative cleanability of different protein products. Product removal from the coupon surface is visually monitored and the time required to clean the spot is recorded as the cleaning time. 7 That bench-scale model uses stainless steel coupons spotted with product samples (Figure 1), followed by cleaning under simulated thermal and chemical conditions that are representative of large-scale cleaning cycles. 4–7 In one of our earlier studies, a scale-down model was developed to study the effect of key operating parameters on the performance of the cleaning process. Bench-scale cleaning studies provide a useful tool to evaluate the relative cleanability of new products and determine the need for revalidation. 2–4 Such an approach, however, requires that the cleanability of all new products be compared to the validated worst case.

#Paired t test jmp verification#

1 Multiproduct facilities can use a worst case–based cleaning validation approach in which cleaning cycles are demonstrated to be capable of cleaning the most difficult-to-clean product no further large-scale verification is needed for other products. Regulatory agencies, therefore, require all biopharmaceutical manufacturing facilities to establish effective and robust cleaning validation programs. This article describes the application of a two-one-sided t-test (TOST) method to assess the comparability of two groups of cleanability data generated from a bench-scale study.Īn effective cleaning process is critical to ensure that product quality attributes are not compromised by contamination or carryover through product contact equipment surfaces shared among different lots. Because of the challenges posed by experimental variability in such evaluations, such relative cleanability assessments should be based on a sound statistical analysis. For a multiproduct facility, bench-scale characterization offers a useful and cost-effective means to support cleaning validation by comparing the cleanability of a new product to a validated one.

Regulatory authorities expect biopharmaceutical manufacturing facilities to demonstrate that they have an effective and consistent cleaning process in place.