getSampleSizeMeans {rpact} | R Documentation |
Returns the sample size for testing means in one or two samples.
getSampleSizeMeans(
design = NULL,
...,
groups = 2,
normalApproximation = FALSE,
meanRatio = FALSE,
thetaH0 = ifelse(meanRatio, 1, 0),
alternative = seq(0.2, 1, 0.2),
stDev = 1,
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 p-values. 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 |
allocationRatioPlanned |
The planned allocation ratio |
At given design the function calculates the stage-wise (non-cumulated) and maximum sample size for testing means. In a two treatment groups design, additionally, an allocation ratio = n1/n2 can be specified. 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. Critical bounds and stopping for futility bounds are provided at the effect scale (mean, mean difference, or mean ratio, respectively) for each sample size calculation separately.
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 sample size functions:
getSampleSizeRates()
,
getSampleSizeSurvival()
# Calculate sample sizes in a fixed sample size parallel group design
# with allocation ratio \code{n1 / n2 = 2} for a range of
# alternative values 1, ..., 5 with assumed standard deviation = 3.5;
# two-sided alpha = 0.05, power 1 - beta = 90%:
getSampleSizeMeans(alpha = 0.05, beta = 0.1, sided = 2, groups = 2,
alternative = seq(1, 5, 1), stDev = 3.5, allocationRatioPlanned = 2)
# Calculate sample sizes in a three-stage Pocock paired comparison design testing
# H0: mu = 2 for a range of alternative values 3,4,5 with assumed standard
# deviation = 3.5; one-sided alpha = 0.05, power 1 - beta = 90%:
getSampleSizeMeans(getDesignGroupSequential(typeOfDesign = "P", alpha = 0.05,
sided = 1, beta = 0.1), groups = 1, thetaH0 = 2,
alternative = seq(3, 5, 1), stDev = 3.5)