Sample Size and Power Calculations |
In a test of equivalence, a treatment mean and a reference mean are compared to each other. Equivalence is taken to be the alternative hypothesis, and the null hypothesis is nonequivalence. The power of a test is the probability of rejecting the null hypothesis when the alternative is true, so in this case, the power is the probability of failing to reject equivalence when the treatments are in fact equivalent, that is, the treatment difference or ratio is within the prespecified boundaries.
The computational details for the power of an equivalence test (refer to Phillips 1990 for the additive model; Diletti, Hauschke, and Steinijans 1991 for the multiplicative) are as follows:
Owen (1965) showed that (t1, t2) has a bivariate noncentral t distribution that can be calculated as the difference of two definite integrals (Owen's Q function):
where is the quantile of a t distribution with df.
and
For equivalence tests, alpha is usually set to 0.05, and power ranges from 0.70 to 0.90 (often set to 0.80).
For the additive model of equivalence, the values you must enter for the null difference, the coefficient of variation (c.v.), and the lower and upper bioequivalence limits must be expressed as percentages of the reference mean. More information on specifications follow:
For the multiplicative model of equivalence, calculate the null ratio as , where is the hypothesized treatment mean and is the hypothesized reference mean. This value is often in the range of 0.80 to 1.20. More information on specifications follow:
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