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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_
)The trial design. If no trial design is specified, a fixed sample size design is used.
In this case, Type I error rate alpha, Type II error rate beta, twoSidedPower,
and sided can be directly entered as argument where necessary.
Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed.
The number of treatment groups (1 or 2), default is 2.
The type of computation of the p-values. If TRUE, the variance is
assumed to be known, default is FALSE, i.e., the calculations are performed
with the t distribution.
If TRUE, the sample size for
one-sided testing of H0: mu1 / mu2 = thetaH0 is calculated, default is FALSE.
The null hypothesis value,
default is 0 for the normal and the binary case (testing means and rates, respectively),
it is 1 for the survival case (testing the hazard ratio).
For non-inferiority designs, thetaH0 is the non-inferiority bound.
That is, in case of (one-sided) testing of
means: a value != 0
(or a value != 1 for testing the mean ratio) can be specified.
rates: a value != 0
(or a value != 1 for testing the risk ratio pi1 / pi2) can be specified.
survival data: a bound for testing H0: hazard ratio = thetaH0 != 1 can be specified.
For testing a rate in one sample, a value thetaH0 in (0, 1) has to be specified for
defining the null hypothesis H0: pi = thetaH0.
The alternative hypothesis value for testing means. This can be a vector of assumed
alternatives, default is seq(0, 1, 0.2) (power calculations) or seq(0.2, 1, 0.2) (sample size calculations).
The standard deviation under which the sample size or power
calculation is performed, default is 1.
If meanRatio = TRUE is specified, stDev defines
the coefficient of variation sigma / mu2.
The planned allocation ratio n1 / n2 for a two treatment groups
design, default is 1. If allocationRatioPlanned = 0 is entered,
the optimal allocation ratio yielding the smallest overall sample size is determined.
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,
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.
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.
Other sample size functions:
getSampleSizeRates(),
getSampleSizeSurvival()
# NOT RUN {
# 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)
# }
# NOT RUN {
# 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)
# }
# NOT RUN {
# }
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