Statistical Calculators › Proportions: One and Two Groups › Equivalence Test for Two Proportions (TOST)
← All Calculators

Proportions: One and Two Groups

Equivalence Test for Two Proportions (TOST)

A two one-sided tests (TOST) calculator for comparing two independent proportions on the absolute difference scale. Uses the unpooled large-sample Z-test, reports both one-sided p-values, and displays the corresponding two-sided confidence interval. Runs entirely in your browser.

Study Data

Enter successes and total subjects for each independent group.
Group 1 — Treatment
Group 2 — Reference
Equivalence specification

Results

Equivalence is concluded only when both one-sided null hypotheses are rejected at the specified alpha level.
Enter study data and click Run Equivalence Test.

Methodology

The TOST procedure tests whether the difference between two independent proportions lies entirely inside a prespecified equivalence interval. Let D = p1 − p2, with lower bound L and upper bound U. The null hypothesis is that the true difference is at or beyond either equivalence boundary; the alternative is that the true difference lies between the boundaries.

Two One-Sided Tests

This calculator uses the unpooled large-sample Z formulation. The estimated proportions are 1 = x1/n1 and 2 = x2/n2. Their difference is D̂ = p̂1 − p̂2.

SE = √[p̂1(1−p̂1)/n1 + p̂2(1−p̂2)/n2]
zL = (D̂ − L) / SE     pL = 1 − Φ(zL)
zU = (D̂ − U) / SE     pU = Φ(zU)

Equivalence is concluded when both p-values are less than alpha. Equivalently, the two-sided 100(1−2α)% confidence interval for the difference lies entirely within the equivalence bounds.

Confidence Interval

The displayed TOST confidence interval uses the same unpooled standard error and a critical value of z1−α:

CI = D̂ ± z1−α × SE

For the default alpha = 0.05, this is a 90% confidence interval. This is the interval associated with the TOST decision, not the conventional 95% interval used for an ordinary two-sided superiority test.

Interpretation

A statistically significant TOST result does not mean that the two observed proportions are exactly equal. It means that the data provide evidence that the true difference is contained within the prespecified interval of clinically acceptable differences. The equivalence margins should therefore be specified from clinical or scientific considerations before examining the observed results.

References

Tunes da Silva, G., Logan, B. R., & Klein, J. P. (2008). Methods for Equivalence and Noninferiority Testing. Biology of Blood and Marrow Transplantation, 15(1 Suppl), 120–127.

Yin, G. (2012). Clinical Trial Design: Bayesian and Frequentist Adaptive Methods. Hoboken, NJ: John Wiley & Sons.

The TOSTER documentation provides a directly comparable TOST implementation for two independent proportions using the normal confidence-interval approach.