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Alexis
Alexis
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You do not compute eps1 and eps2 because these are researcher choices.

Equivalence testing (both the lower powered Two One-Sided Tests, or TOST framework, and the uniformly most powerful, or UMP test framework professed by Wellek) rely on a notion of equivalence threshold, or the *minimumminimum effect size that you the researcher find to be relevant (possibly informed by regulatory guidelines, as is the case with the FDA some some applications of bioequivalence in the United States). Put another way, the researcher effectively states a priori effectively states "I do not care about effect sizes smaller the equivalence threshold." I do not know what your data are, but, for example, when studying whether a policy changes national incidence rates of, say, HIV a researcher might say a priori "I do not care about effect sizes smaller than $\pm$1 new case of HIV per 100,000 people per year."

Some equivalence thresholds are expressed symmetrically on an absolute or relative scale. Some are expressed asymmetrically. The values of esp1 and esp2 are non-negative values that express what you find to be the appropriate boundary between relevant and equivalent, and correspond to your choices for the values of $\varepsilon_1^{\prime}$ and $\varepsilon_2^{\prime}$ in expression 6.10 on page 126 of Wellek (Second ed.).

You do not compute eps1 and eps2 because these are researcher choices.

Equivalence testing (both the lower powered Two One-Sided Tests, or TOST framework, and the uniformly most powerful, or UMP test framework professed by Wellek) rely on a notion of equivalence threshold, or the *minimum effect size that you the researcher find to be relevant (possibly informed by regulatory guidelines, as is the case with the FDA some some applications of bioequivalence in the United States). Put another way, the researcher a priori effectively states "I do not care about effect sizes smaller the equivalence threshold." I do not know what your data are, but, for example, when studying whether a policy changes national incidence rates of, say, HIV a researcher might say a priori "I do not care about effect sizes smaller than $\pm$1 new case of HIV per 100,000 people per year."

Some equivalence thresholds are expressed symmetrically on an absolute or relative scale. Some are expressed asymmetrically. The values of esp1 and esp2 are non-negative values that express what you find to be the appropriate boundary between relevant and equivalent, and correspond to your choices for the values of $\varepsilon_1^{\prime}$ and $\varepsilon_2^{\prime}$ in expression 6.10 on page 126 of Wellek (Second ed.).

You do not compute eps1 and eps2 because these are researcher choices.

Equivalence testing (both the lower powered Two One-Sided Tests, or TOST framework, and the uniformly most powerful, or UMP test framework professed by Wellek) rely on a notion of equivalence threshold, or the minimum effect size that you the researcher find to be relevant (possibly informed by regulatory guidelines, as is the case with the FDA some some applications of bioequivalence in the United States). Put another way, the researcher effectively states a priori "I do not care about effect sizes smaller the equivalence threshold." I do not know what your data are, but, for example, when studying whether a policy changes national incidence rates of, say, HIV a researcher might say a priori "I do not care about effect sizes smaller than $\pm$1 new case of HIV per 100,000 people per year."

Some equivalence thresholds are expressed symmetrically on an absolute or relative scale. Some are expressed asymmetrically. The values of esp1 and esp2 are non-negative values that express what you find to be the appropriate boundary between relevant and equivalent, and correspond to your choices for the values of $\varepsilon_1^{\prime}$ and $\varepsilon_2^{\prime}$ in expression 6.10 on page 126 of Wellek (Second ed.).

Source Link
Alexis
Alexis
  • 30.7k
  • 8
  • 101
  • 176

You do not compute eps1 and eps2 because these are researcher choices.

Equivalence testing (both the lower powered Two One-Sided Tests, or TOST framework, and the uniformly most powerful, or UMP test framework professed by Wellek) rely on a notion of equivalence threshold, or the *minimum effect size that you the researcher find to be relevant (possibly informed by regulatory guidelines, as is the case with the FDA some some applications of bioequivalence in the United States). Put another way, the researcher a priori effectively states "I do not care about effect sizes smaller the equivalence threshold." I do not know what your data are, but, for example, when studying whether a policy changes national incidence rates of, say, HIV a researcher might say a priori "I do not care about effect sizes smaller than $\pm$1 new case of HIV per 100,000 people per year."

Some equivalence thresholds are expressed symmetrically on an absolute or relative scale. Some are expressed asymmetrically. The values of esp1 and esp2 are non-negative values that express what you find to be the appropriate boundary between relevant and equivalent, and correspond to your choices for the values of $\varepsilon_1^{\prime}$ and $\varepsilon_2^{\prime}$ in expression 6.10 on page 126 of Wellek (Second ed.).