getSampleSizeCounts {rpact}  R Documentation 
Returns the sample size for testing the ratio of mean rates of negative binomial distributed event numbers in two samples at given effect.
getSampleSizeCounts(
design = NULL,
...,
lambda1 = NA_real_,
lambda2 = NA_real_,
lambda = NA_real_,
theta = NA_real_,
thetaH0 = 1,
overdispersion = 0,
fixedExposureTime = NA_real_,
accrualTime = NA_real_,
accrualIntensity = NA_real_,
followUpTime = NA_real_,
maxNumberOfSubjects = NA_integer_,
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. 
lambda1 
A numeric value or vector that represents the assumed rate of a homogeneous Poisson process in the active treatment group, there is no default. 
lambda2 
A numeric value that represents the assumed rate of a homogeneous Poisson process in the control group, there is no default. 
lambda 
A numeric value or vector that represents the assumed rate of a homogeneous Poisson process in the pooled treatment groups, there is no default. 
theta 
A numeric value or vector that represents the assumed mean ratios lambda1/lambda2 of a homogeneous Poisson process, there is no default. 
thetaH0 
The null hypothesis value,
default is
For testing a rate in one sample, a value 
overdispersion 
A numeric value that represents the assumed overdispersion of the negative binomial distribution,
default is 
fixedExposureTime 
If specified, the fixed time of exposure per subject for count data, there is no default. 
accrualTime 
If specified, the assumed accrual time interval(s) for the study, there is no default. 
accrualIntensity 
If specified, the assumed accrual intensities for the study, there is no default. 
followUpTime 
If specified, the assumed (additional) followup time for the study, there is no default.
The total study duration is 
maxNumberOfSubjects 

allocationRatioPlanned 
The planned allocation ratio 
At given design the function calculates the information, and stagewise and maximum sample size for testing mean rates
of negative binomial distributed event numbers in two samples at given effect.
The sample size calculation is performed either for a fixed exposure time or a variable exposure time with fixed followup.
For the variable exposure time case, at given maximum sample size the necessary followup time is calculated.
The planned calendar time of interim stages is calculated if an accrual time is defined.
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
can also be specified.
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()
,
getSampleSizeRates()
,
getSampleSizeSurvival()
# Fixed sample size trial where a therapy is assumed to decrease the
# exacerbation rate from 1.4 to 1.05 (25% decrease) within an observation
# period of 1 year, i.e., each subject has an equal followup of 1 year.
# The sample size that yields 90% power at significance level 0.025 for
# detecting such a difference, if the overdispersion is assumed to be
# equal to 0.5, is obtained by
getSampleSizeCounts(alpha = 0.025, beta = 0.1, lambda2 = 1.4,
theta = 0.75, overdispersion = 0.5, fixedExposureTime = 1)
## Not run:
# Noninferiority test with blinded sample size reassessment to reproduce
# Table 2 from Friede and Schmidli (2010):
getSampleSizeCounts(alpha = 0.025, beta = 0.2, lambda = 1, theta = 1,
thetaH0 = 1.15, overdispersion = 0.4, fixedExposureTime = 1)
# Group sequential alpha and beta spending function design with O'Brien and
# Fleming type boundaries: Estimate observation time under uniform
# recruitment of patients over 6 months and a fixed exposure time of 12
# months (lambda1, lambda2, and overdispersion as specified):
getSampleSizeCounts(design = getDesignGroupSequential(
kMax = 3, alpha = 0.025, beta = 0.2,
typeOfDesign = "asOF", typeBetaSpending = "bsOF"),
lambda1 = 0.2, lambda2 = 0.3, overdispersion = 1,
fixedExposureTime = 12, accrualTime = 6)
# Group sequential alpha spending function design with O'Brien and Fleming
# type boundaries: Sample size for variable exposure time with uniform
# recruitment over 1.25 months and study time (accrual + followup) = 4
# (lambda1, lambda2, and overdispersion as specified, no futility stopping):
getSampleSizeCounts(design = getDesignGroupSequential(
kMax = 3, alpha = 0.025, beta = 0.2, typeOfDesign = "asOF"),
lambda1 = 0.0875, lambda2 = 0.125, overdispersion = 5,
followUpTime = 2.75, accrualTime = 1.25)
## End(Not run)