CVfromCI | R Documentation |
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)
pe |
Point estimate of the T/R ratio. |
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. |
design |
Character string describing the study design. |
alpha |
Error probability. Set it to |
robust |
With |
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%
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