# NOT RUN {
# Example 1
# 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
# 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),
# and repeated 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))
# }
# NOT RUN {
# }
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