getSampleSizeRates {rpact} | R Documentation |
Returns the sample size for testing rates in one or two samples.
getSampleSizeRates(
design = NULL,
...,
groups = 2,
normalApproximation = TRUE,
riskRatio = FALSE,
thetaH0 = ifelse(riskRatio, 1, 0),
pi1 = c(0.4, 0.5, 0.6),
pi2 = 0.2,
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 |
If |
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 |
allocationRatioPlanned |
The planned allocation ratio |
At given design the function calculates the stage-wise (non-cumulated) and maximum sample size for testing rates. In a two treatment groups design, additionally, an allocation ratio = n1/n2 can be specified. If a null hypothesis value thetaH0 != 0 for testing the difference of two rates thetaH0 != 1 for testing the risk ratio is specified, the sample size formula according to Farrington & Manning (Statistics in Medicine, 1990) is used. Critical bounds and stopping for futility bounds are provided at the effect scale (rate, rate difference, or rate ratio, respectively) for each sample size calculation separately. For the two-sample case, the calculation here is performed at fixed pi2 as given as argument in the function.
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:
getSampleSizeMeans()
,
getSampleSizeSurvival()
# Calculate the stage-wise sample sizes, maximum sample sizes, and the optimum
# allocation ratios for a range of pi1 values when testing
# H0: pi1 - pi2 = -0.1 within a two-stage O'Brien & Fleming design;
# alpha = 0.05 one-sided, power 1 - beta = 90%:
getSampleSizeRates(getDesignGroupSequential(kMax = 2, alpha = 0.05,
beta = 0.1), groups = 2, thetaH0 = -0.1, pi1 = seq(0.4, 0.55, 0.025),
pi2 = 0.4, allocationRatioPlanned = 0)
# Calculate the stage-wise sample sizes, maximum sample sizes, and the optimum
# allocation ratios for a range of pi1 values when testing
# H0: pi1 / pi2 = 0.80 within a three-stage O'Brien & Fleming design;
# alpha = 0.025 one-sided, power 1 - beta = 90%:
getSampleSizeRates(getDesignGroupSequential(kMax = 3, alpha = 0.025,
beta = 0.1), groups = 2, riskRatio = TRUE, thetaH0 = 0.80,
pi1 = seq(0.3, 0.5, 0.025), pi2 = 0.3, allocationRatioPlanned = 0)