equivalence (version 0.7.2)

tost.stat: Computes a TOST for equivalence from sample statistics

Description

This function computes the test and key test quantities for the two one-sided test for equivalence, as documented in Schuirmann (1981) and Westlake (1981). This function computes the test from the statistics of a sample of paired differences of a normally-distributed population.

Usage

tost.stat(mean, std, n, null = 0, alpha = 0.05, Epsilon = 0.36)

Arguments

mean

sample mean

std

sample standard deviation

n

sample size

null

the value of the parameter in the null hypothesis

alpha

test size

Epsilon

magnitude of region of similarity

Value

A list with the following components

Dissimilarity

the outcome of the test of the null hypothesis of dissimilarity

Mean

the mean of the sample

StdDev

the standard deviation of the sample

n

the non-missing sample size

alpha

the size of the test

Epsilon

the magnitude of the region of similarity

Interval

the half-length of the two one-sided interval

Details

This test requires the assumption of normality of the population.

References

Schuirmann, D.L. 1981. On hypothesis testing to determine if the mean of a normal distribution is contained in a known interval. Biometrics 37 617.

Wellek, S. 2003. Testing statistical hypotheses of equivalence. Chapman and Hall/CRC. 284 pp.

Westlake, W.J. 1981. Response to T.B.L. Kirkwood: bioequivalence testing - a need to rethink. Biometrics 37, 589-594.

See Also

tost.data, ptte.stat

Examples

data(ufc)
tost.stat(mean(ufc$Height.m.p - ufc$Height.m, na.rm=TRUE),
  sd(ufc$Height.m.p - ufc$Height.m, na.rm=TRUE),
  sum(!is.na(ufc$Height.m.p - ufc$Height.m)))