getPowerMeans {rpact}  R Documentation 
Returns the power, stopping probabilities, and expected sample size for testing means in one or two samples at given maximum sample size.
getPowerMeans(
design = NULL,
...,
groups = 2L,
normalApproximation = FALSE,
meanRatio = FALSE,
thetaH0 = ifelse(meanRatio, 1, 0),
alternative = seq(0, 1, 0.2),
stDev = 1,
directionUpper = NA,
maxNumberOfSubjects = NA_real_,
allocationRatioPlanned = NA_real_
)
design 
The trial design. If no trial design is specified, a fixed sample size design is used.
In this case, Type I error rate 
... 
Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed. 
groups 
The number of treatment groups (1 or 2), default is 
normalApproximation 
The type of computation of the pvalues. If 
meanRatio 
If 
thetaH0 
The null hypothesis value,
default is
For testing a rate in one sample, a value 
alternative 
The alternative hypothesis value for testing means. This can be a vector of assumed
alternatives, default is 
stDev 
The standard deviation under which the sample size or power
calculation is performed, default is 
directionUpper 
Logical. Specifies the direction of the alternative,
only applicable for onesided testing; default is 
maxNumberOfSubjects 

allocationRatioPlanned 
The planned allocation ratio 
At given design the function calculates the power, stopping probabilities,
and expected sample size for testing means at given sample size.
In a two treatment groups design, additionally, an allocation ratio = n1 / n2
can be specified where n1
and n2
are the number
of subjects in the two treatment groups.
A null hypothesis value thetaH0 != 0 for testing the difference of two means
or thetaH0 != 1
for testing the ratio of two means can be specified.
For the specified sample size, critical bounds and stopping for futility
bounds are provided at the effect scale (mean, mean difference, or
mean ratio, respectively)
Returns a TrialDesignPlan
object.
The following generics (R generic functions) are available for this result object:
names()
to obtain the field names,
print()
to print the object,
summary()
to display a summary of the object,
plot()
to plot the object,
as.data.frame()
to coerce the object to a data.frame
,
as.matrix()
to coerce the object to a matrix
.
Click on the link of a generic in the list above to go directly to the help documentation of
the rpact
specific implementation of the generic.
Note that you can use the R function methods
to get all the methods of a generic and
to identify the object specific name of it, e.g.,
use methods("plot")
to get all the methods for the plot
generic.
There you can find, e.g., plot.AnalysisResults
and
obtain the specific help documentation linked above by typing ?plot.AnalysisResults
.
Other power functions:
getPowerCounts()
,
getPowerRates()
,
getPowerSurvival()
# Calculate the power, stopping probabilities, and expected sample size
# for testing H0: mu1  mu2 = 0 in a twoarmed design against a range of
# alternatives H1: mu1  m2 = delta, delta = (0, 1, 2, 3, 4, 5),
# standard deviation sigma = 8, maximum sample size N = 80 (both treatment
# arms), and an allocation ratio n1/n2 = 2. The design is a three stage
# O'Brien & Fleming design with nonbinding futility bounds (0.5, 0.5)
# for the two interims. The computation takes into account that the t test
# is used (normalApproximation = FALSE).
getPowerMeans(getDesignGroupSequential(alpha = 0.025,
sided = 1, futilityBounds = c(0.5, 0.5)),
groups = 2, alternative = c(0:5), stDev = 8,
normalApproximation = FALSE, maxNumberOfSubjects = 80,
allocationRatioPlanned = 2)