getPowerRates {rpact}  R Documentation 
Returns the power, stopping probabilities, and expected sample size for testing rates in one or two samples at given maximum sample size.
getPowerRates(
design = NULL,
...,
groups = 2L,
riskRatio = FALSE,
thetaH0 = ifelse(riskRatio, 1, 0),
pi1 = seq(0.2, 0.5, 0.1),
pi2 = 0.2,
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 
riskRatio 
If 
thetaH0 
The null hypothesis value,
default is
For testing a rate in one sample, a value 
pi1 
A numeric value or vector that represents the assumed probability in
the active treatment group if two treatment groups
are considered, or the alternative probability for a one treatment group design,
default is 
pi2 
A numeric value that represents the assumed probability in the reference group if two treatment
groups are considered, 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 rates at given maximum sample size.
The sample sizes over the stages are calculated according to the specified information rate in the design.
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.
If a null hypothesis value thetaH0 != 0 for testing the difference of two rates
or thetaH0 != 1
for testing the risk ratio is specified, the
formulas according to Farrington & Manning (Statistics in Medicine, 1990) are used (only onesided testing).
Critical bounds and stopping for futility bounds are provided at the effect scale
(rate, rate difference, or rate ratio, respectively).
For the twosample case, the calculation here is performed at fixed pi2 as given as argument in the function.
Note that the power calculation for rates is always based on the normal approximation.
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()
,
getPowerMeans()
,
getPowerSurvival()
# Calculate the power, stopping probabilities, and expected sample size in a
# twoarmed design at given maximum sample size N = 200 in a threestage
# O'Brien & Fleming design with information rate vector (0.2,0.5,1),
# nonbinding futility boundaries (0,0), i.e., the study stops for futility
# if the pvalue exceeds 0.5 at interm, and allocation ratio = 2 for a range
# of pi1 values when testing H0: pi1  pi2 = 0.1:
getPowerRates(getDesignGroupSequential(informationRates = c(0.2, 0.5, 1),
futilityBounds = c(0, 0)), groups = 2, thetaH0 = 0.1,
pi1 = seq(0.3, 0.6, 0.1), directionUpper = FALSE,
pi2 = 0.7, allocationRatioPlanned = 2, maxNumberOfSubjects = 200)
## Not run:
# Calculate the power, stopping probabilities, and expected sample size in a single
# arm design at given maximum sample size N = 60 in a threestage twosided
# O'Brien & Fleming design with information rate vector (0.2, 0.5,1)
# for a range of pi1 values when testing H0: pi = 0.3:
getPowerRates(getDesignGroupSequential(informationRates = c(0.2, 0.5,1),
sided = 2), groups = 1, thetaH0 = 0.3, pi1 = seq(0.3, 0.5, 0.05),
maxNumberOfSubjects = 60)
## End(Not run)