CVfromCI {PowerTOST}

CV from a given Confidence interval

Description

Calculates the CV (coefficient of variation) from a known confidence interval of a BE study.
Useful if no CV but the 90% CI was given in literature.

Usage

CVfromCI(pe, lower, upper, n, design = "2x2", alpha = 0.05, robust = FALSE)
CI2CV(pe, lower, upper, n, design = "2x2", alpha = 0.05, robust = FALSE)

Arguments

pe

Point estimate of the T/R ratio.
The pe may be missing. In that case it will be calculated as geometric mean
of lower and upper.

lower

Lower confidence limit of the BE ratio.

upper

Upper confidence limit of the BE ratio.

n

Total number of subjects under study if given as scalar.
Number of subjects in (sequence) groups if given as vector.

design

Character string describing the study design.
See known.designs() for designs covered in this package.

alpha

Error probability. Set it to (1-confidence)/2 (i.e. to 0.05 for the usual 90% confidence intervals).

robust

With robust=FALSE (the default) usual degrees of freedom of the designs are used.
With robust=TRUE the degrees of freedom for the so-called robust evaluation (df2 in known.designs()) will be used. This may be helpful if the CI was evaluated via a mixed model or via intra-subject contrasts (aka Senn’s basic estimator).

Details

See Helmut Schütz’ presentation for the algebra underlying this function.

Value

Numeric value of the CV as ratio.

Note

The calculations are based on the assumption of evaluation via log-transformed values.
The calculations are further based on a common variance of Test and Reference treatments in replicate crossover studies or parallel group study, respectively.

In case of argument n given as n(total) and is not divisible by the number of (sequence) groups the total sample size is partitioned to the (sequence) groups to have small imbalance only. A message is given in such cases.
The estimated CV is conservative (i.e., higher than actually observed) in case of unbalancedness.

CI2CV() is simply an alias to CVfromCI().

Author(s)

Original by D. Labes with suggestions by H. Schütz.
Reworked and adapted to unbalanced studies by B. Lang.

References

Yuan J, Tong T, Tang M-L. Sample Size Calculation for Bioequivalence Studies Assessing Drug Effect and Food Effect at the Same Time With a 3-Treatment Williams Design. Regul Sci. 2013;47(2):242–7. doi:10.1177/2168479012474273

Examples

# Given a 90% confidence interval (without point estimate) 
# from a classical 2x2 crossover with 22 subjects
CVfromCI(lower=0.91, upper=1.15, n=22, design="2x2")
# will give [1] 0.2279405, i.e a CV ~ 23%
#
# unbalanced 2x2 crossover study, but not reported as  such
CI2CV(lower=0.89, upper=1.15, n=24)
# will give a CV ~ 26.3%
# unbalancedness accounted for
CI2CV(lower=0.89, upper=1.15, n=c(16,8))
# should give CV ~ 24.7%