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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 BE 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<U+2019>s basic estimator).
Numeric value of the CV as ratio.
See Helmut Sch<U+00FC>tz<U+2019> lecture for a description of the algebra underlying this function.
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. 10.1177/2168479012474273
# NOT RUN {
# 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%
# }
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