View source: R/expSampleSize_noninf.R
expsampleN.noninf  R Documentation 
Estimates the sample size based on the expected power for a variety of designs used in bioequivalence studies. See known.designs for the study designs covered.
expsampleN.noninf(alpha = 0.025, targetpower = 0.8, logscale = TRUE, theta0, margin, CV, design = "2x2", robust = FALSE, prior.type = c("CV", "theta0", "both"), prior.parm = list(), method = c("exact", "approx"), print = TRUE, details)
alpha 
Significance level (onesided). Defaults here to 0.025. 
targetpower 
Power to achieve at least. Must be 
logscale 
Should the data used on logtransformed or on original scale?

theta0 
Assumed ‘true’ (or ‘observed’ in case of 
margin 
Noninferiority margin. 
CV 
In case of If In case of crossover studies this is the withinsubject CV, in case of a parallelgroup design the CV of the total variability. 
design 
Character string describing the study design. 
robust 
Defaults to 
prior.type 
Specifies which parameter uncertainty should be accounted for.
In case of 
prior.parm 
A list of parameters expressing the prior information about the
variability and/or treatment effect. Possible components are 
method 
Defaults to 
print 
If 
details 
If 
The sample size is estimated based on iterative evaluation of
expected power. The starting value of the sample size search is
taken from a large sample approximation if prior.type="CV"
.
Else an empirical start value is obtained. Note that in case of
prior.type="both"
the calculation may still take several seconds.
Note also that the expected power is always bounded above by the
socalled probability of technical success (PTS) which
may be a value less than 1.Therefore, it may be possible that it
is either not possible to calculate the required sample size at
all or that the sample size gets very large if the given targetpower
is less but close to the PTS.
Notes on the underlying hypotheses
If the supplied margin is < 0
(logscale=FALSE
) or
< 1
(logscale=TRUE
), then it is assumed higher response
values are better. The hypotheses are
H0: theta0 <= margin
H1: theta0 > margin
where theta0 = mean(test)mean(reference)
if logscale=FALSE
or
H0: log(theta0) <= log(margin)
H1: log(theta0) > log(margin)
where theta0 = mean(test)/mean(reference)
if logscale=TRUE
.
If the supplied margin is > 0
(logscale=FALSE
) or
> 1
(logscale=TRUE
), then it is assumed lower response
values are better. The hypotheses are
H0: theta0 >= margin
H1: theta0 < margin
where theta0 = mean(test)mean(reference)
if logscale=FALSE
or
H0: log(theta0) >= log(margin)
H1: log(theta0) < log(margin)
where theta0 = mean(test)/mean(reference)
if logscale=TRUE
.
This latter case may also be considered as ‘nonsuperiority’.
A data.frame with the input values and the result of the sample
size estimation.
The Sample size
column contains the total sample
size in case of all designs implemented.
B. Lang, D. Labes
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exppower.noninf, known.designs, sampleN.noninf
# Classical 2x2 crossover, target power = 80%, # assumed true ratio = 95%, margin = 0.8, # intrasubject CV=30% estimated from prior 2x2 trial # with m = 12 subjects expsampleN.noninf(theta0 = 0.95, margin = 0.8, CV = 0.3, design = "2x2", prior.parm = list(m = 12, design = "2x2")) # gives n = 58 with achieved expected power 0.809148 # Compare this to the usual sample size with CV assumed # as 'carved in stone' sampleN.noninf(theta0 = 0.95, margin = 0.8, CV = 0.3) # Perform 'nonsuperiority' (lower is better) with assumed # true ratio = 105% and margin 125% expsampleN.noninf(theta0 = 1.05, margin = 1.25, CV = 0.3, design = "2x2", prior.parm = list(m = 12, design = "2x2")) # should give n = 56 with achieved expected power 0.806862 # More than one CV with corresponding degrees of freedom # other settings as above in first example CVs < c(0.25, 0.3) dfs < c(22, 10) expsampleN.noninf(theta0 = 0.95, margin = 0.8, CV = CVs, prior.parm = list(df = dfs)) # should give a pooled CV=0.2664927 with 32 df and a sample # size n=42 with achieved expected power 0.814073 exact # achieved expected power 0.816163 approximate acc. to Julious # Uncertainty is accounted for CV and theta0 expsampleN.noninf(CV = 0.3, prior.type = "both", prior.parm = list(m = 12, design = "2x2")) # gives a dramatic increase in sample size (n = 194) # due to small pilot trial
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