The test sample is often from a new or unproven product or process. For example, the test sample in a pharmaceutical study might be a new generic drug that you hope to prove is as effective as the leading brand name (reference) drug.
The reference sample is often from a proven product or process. For example, the reference sample in a pharmaceutical study might be a drug that has already been shown to have the desired effect.
C1 | C2 |
---|---|
New Device | Current Device |
103 | 100 |
68 | 71 |
95 | 93 |
137 | 133 |
From the drop-down list, indicate how you want to express your equivalence criteria.
Define equivalence in terms of a difference between the mean of the test population and the mean of the reference population.
Define equivalence in terms of the ratio of the mean of the test population to the mean of the reference population.
Define equivalence in terms of the ratio of the mean of the test population to the mean of the reference population, as modeled with a log transformation of the original data. For this option, all observations must be greater than 0.
From the drop-down list, select the hypothesis that you want to prove or demonstrate.
To test the difference between the test mean and the reference mean, select one of the following options.
Test whether the difference between the population means is within the limits that you specify.
For example, an analyst measures blood glucose levels twice in the same group of patients, using two different devices. The analyst wants to determine whether the mean glucose reading for the new device is within ± 20% of the mean glucose reading of the currently approved device.
Test whether the mean of the test population is greater than the mean of the reference population.
For example, an analyst wants to determine whether a new blade cuts samples of leather better than the currently used blade.
Test whether the mean of the test population is less than the mean of the reference population.
For example, an analyst wants to demonstrate that a new medication takes effect in less time, on average, than the current medication.
Test whether the difference between the population means is greater than a lower limit.
For example, a researcher wants to determine whether the mean reduction in diastolic blood pressure induced by an experimental drug is more than 3 mm Hg greater than the mean reduction induced by the current medication.
Test whether the difference between the population means is less than an upper limit.
For example, researchers develop a new formulation of a popular medication. The new formulation is less expensive, but requires more time to achieve maximum effect. Researchers want to ensure that the mean difference in time to maximum effect does not exceed that of the current medication by more than 2 minutes.
To test the ratio of the test mean to the reference mean, select one of the following options.
Test whether the ratio of the population means is within the limits that you specify. Both limits must be greater than 0. A ratio of 1 indicates that the two means are equal.
For example, an analyst measures blood glucose levels twice in the same group of patients, using two different devices. The analyst wants to determine whether the mean glucose reading for the new device is between 95% and 105% of the mean glucose reading of the currently approved device.
Test whether the ratio of the population means is greater than a lower limit.
For example, a researcher wants to determine whether the mean reduction in diastolic blood pressure induced by an experimental drug is more than 1.5 times greater than the mean reduction induced by the current medication.
Test whether the ratio of the population means is less than an upper limit.
For example, an analyst wants to prove that the mean response time for a new therapy does not exceed the response time for an established therapy by 5% or more. The analyst tests whether the ratio of the mean response times is less than 1.05.
To test the ratio of the test mean to the reference mean using a log transformation of the original data, select one of the following options.
Test whether the ratio of the population means is within the limits that you specify. Both limits must be greater than 0. A ratio of 1 indicates that the two means are equal.
For example, an analyst needs to demonstrate that the mean bioavailability of a test formulation is within 80% (0.8) and 125% (1.25) that of the reference formulation, using log transformed data.
Test whether the ratio of the population means is greater than a lower limit.
For example, an analyst needs to demonstrate that the mean bioavailability of a test formulation is greater than 80% (0.8) that of the reference formulation, using log transformed data.
Test whether the ratio of the population means is less than an upper limit.
For example, an analyst needs to demonstrate that the mean bioavailability of a test formulation is less than 125% (1.25) that of the reference formulation, using log transformed data.
Enter a value for each equivalence limit that is included in the alternative hypothesis.
Enter the lowest acceptable value for the difference or ratio. You want to demonstrate that the difference (or ratio) between the mean of the test population and the mean of the reference population is not lower than this value.
Enter the highest acceptable value for the difference or ratio. You want to demonstrate that the difference (or ratio) between the mean of the test population and the mean of the reference population does not exceed this value.
Select this option to specify that the limit represents a proportion of the reference mean. Use to test whether the mean of the test population is within a certain percentage of the mean of the reference population. For example, select this option to change the limit from a fixed value of 0.1 to a value that equals 10% of the reference mean.
This option is displayed only when you express equivalence in terms of a difference between the test mean and the reference mean.