PowerTOST (version 1.4-6)

# CVfromCI: 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 BE 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`. Is 0.05 for the usual 90% confidence intervals.

robust

With `robust=FALSE` the 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 mixed model or via intra-subject contrasts (aka Senn's basic estimator).

## Value

Numeric value of the CV as ratio.

## Details

See Helmut Schuetz lectures at www.bebac.at/lectures.htm for a description of the algebra underlying this function.

## Examples

Run this code
``````# 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  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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