CVfromCI: CV from a given Confidence interval

View source: R/CVfromCI_BL.R

CVfromCIR Documentation

CV from a given Confidence interval


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.


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



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 confidence limit of the BE ratio.


Upper confidence limit of the BE ratio.


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


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


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


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).


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


Numeric value of the CV as ratio.


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().


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


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


# 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%

PowerTOST documentation built on March 18, 2022, 5:47 p.m.