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# Example 1 One-Sample Test
# Perform an analysis within a three-stage group sequential design with
# O'Brien & Fleming boundaries and one-sample data with a continuous outcome
# where H0: mu = 1.2 is to be tested
dsnGS <- getDesignGroupSequential()
dataMeans <- getDataset(
n = c(30,30),
means = c(1.96,1.76),
stDevs = c(1.92,2.01))
getAnalysisResults(design = dsnGS, dataInput = dataMeans, thetaH0 = 1.2)
# You can obtain the results when performing an inverse normal combination test
# with these data by using the commands
dsnIN <- getDesignInverseNormal()
getAnalysisResults(design = dsnIN, dataInput = dataMeans, thetaH0 = 1.2)
# Example 2 Use Function Approach with Time to Event Data
# Perform an analysis within a use function approach according to an
# O'Brien & Fleming type use function and survival data where
# where H0: hazard ratio = 1 is to be tested. The events were observed
# over time and maxInformation = 120, informationEpsilon = 5 specifies
# that 116 > 120 - 5 observed events defines the final analysis.
design <- getDesignGroupSequential(typeOfDesign = "asOF")
dataSurvival <- getDataset(
overallEvents = c(33, 72, 116),
overallLogRanks = c(1.33, 1.88, 1.902))
getAnalysisResults(design, dataInput = dataSurvival, maxInformation = 120,
informationEpsilon = 5)
# Example 3 Multi-Arm Design
# In a four-stage combination test design with O'Brien & Fleming boundaries
# at the first stage the second treatment arm was dropped. With the Bonferroni
# intersection test, the results together with the CRP, conditional power
# (assuming a total of 40 subjects for each comparison and effect sizes 0.5
# and 0.8 for treatment arm 1 and 3, respectively, and standard deviation 1.2),
# RCIs and p-values of a closed adaptive test procedure are
# obtained as follows with the given data (treatment arm 4 refers to the
# reference group (displayed with summary and plot commands):
data <- getDataset(
n1 = c(22, 23),
n2 = c(21, NA),
n3 = c(20, 25),
n4 = c(25, 27),
means1 = c(1.63, 1.51),
means2 = c(1.4, NA),
means3 = c(0.91, 0.95),
means4 = c(0.83, 0.75),
stds1 = c(1.2, 1.4),
stds2 = c(1.3, NA),
stds3 = c(1.1, 1.14),
stds4 = c(1.02, 1.18))
design <- getDesignInverseNormal(kMax = 4)
x <- getAnalysisResults(design, dataInput = data, intersectionTest = "Bonferroni",
nPlanned = c(40, 40), thetaH1 = c(0.5, NA, 0.8), assumedStDevs = 1.2)
summary(x)
plot(x, thetaRange = c(0,0.8))
design <- getDesignConditionalDunnett(secondStageConditioning = FALSE)
y <- getAnalysisResults(design, dataInput = data,
nPlanned = c(40), thetaH1 = c(0.5, NA, 0.8), assumedStDevs = 1.2, stage = 1)
summary(y)
plot(y, thetaRange = c(0,0.4))
# Example 4 Enrichment Design
# Perform an two-stage enrichment design analysis with O'Brien & Fleming boundaries
# where one sub-population (S1) and a full population (F) are considered as primary
# analysis sets. At interim, S1 is selected for further analysis and the sample
# size is increased accordingly. With the Spiessens & Debois intersection test,
# the results of a closed adaptive test procedure together with the CRP, repeated
# RCIs and p-values are obtained as follows with the given data (displayed with
# summary and plot commands):
design <- getDesignInverseNormal(kMax = 2, typeOfDesign = "OF")
dataS1 <- getDataset(
means1 = c(13.2, 12.8),
means2 = c(11.1, 10.8),
stDev1 = c(3.4, 3.3),
stDev2 = c(2.9, 3.5),
n1 = c(21, 42),
n2 = c(19, 39))
dataNotS1 <- getDataset(
means1 = c(11.8, NA),
means2 = c(10.5, NA),
stDev1 = c(3.6, NA),
stDev2 = c(2.7, NA),
n1 = c(15, NA),
n2 = c(13, NA))
dataBoth <- getDataset(S1 = dataS1, R = dataNotS1)
x <- getAnalysisResults(design, dataInput = dataBoth,
intersectionTest = "SpiessensDebois",
varianceOption = "pooledFromFull",
stratifiedAnalysis = TRUE)
summary(x)
plot(x, type = 2)
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# }
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