| getAnalysisResults {rpact} | R Documentation |
Calculates and returns the analysis results for the specified design and data.
getAnalysisResults(
design,
dataInput,
...,
directionUpper = NA,
thetaH0 = NA_real_,
nPlanned = NA_real_,
allocationRatioPlanned = 1,
stage = NA_integer_,
maxInformation = NULL,
informationEpsilon = NULL
)
design |
The trial design. |
dataInput |
The summary data used for calculating the test results.
This is either an element of |
... |
Further arguments to be passed to methods (cf., separate functions in "See Also" below), e.g.,
|
directionUpper |
Logical. Specifies the direction of the alternative,
only applicable for one-sided testing; default is |
thetaH0 |
The null hypothesis value,
default is
For testing a rate in one sample, a value |
nPlanned |
The additional (i.e., "new" and not cumulative) sample size planned for each of the subsequent stages. The argument must be a vector with length equal to the number of remaining stages and contain the combined sample size from both treatment groups if two groups are considered. For survival outcomes, it should contain the planned number of additional events. For multi-arm designs, it is the per-comparison (combined) sample size. For enrichment designs, it is the (combined) sample size for the considered sub-population. |
allocationRatioPlanned |
The planned allocation ratio |
stage |
The stage number (optional). Default: total number of existing stages in the data input. |
maxInformation |
Positive integer value specifying the maximum information. |
informationEpsilon |
Positive integer value specifying the absolute information epsilon, which
defines the maximum distance from the observed information to the maximum information that causes the final analysis.
Updates at the final analysis in case the observed information at the final
analysis is smaller ("under-running") than the planned maximum information |
Given a design and a dataset, at given stage the function calculates the test results (effect sizes, stage-wise test statistics and p-values, overall p-values and test statistics, conditional rejection probability (CRP), conditional power, Repeated Confidence Intervals (RCIs), repeated overall p-values, and final stage p-values, median unbiased effect estimates, and final confidence intervals.
For designs with more than two treatments arms (multi-arm designs) or enrichment designs a closed combination test is performed. That is, additionally the statistics to be used in a closed testing procedure are provided.
The conditional power is calculated if the planned sample size for the subsequent stages (nPlanned)
is specified. The conditional power is calculated either under the assumption of the observed effect or
under the assumption of an assumed effect, that has to be specified (see above).
For testing rates in a two-armed trial, pi1 and pi2 typically refer to the rates in the treatment
and the control group, respectively. This is not mandatory, however, and so pi1 and pi2 can be interchanged.
In many-to-one multi-armed trials, piTreatments and piControl refer to the rates in the treatment arms and
the one control arm, and so they cannot be interchanged. piTreatments and piControls in enrichment designs
can principally be interchanged, but we use the plural form to indicate that the rates can be differently
specified for the sub-populations.
Median unbiased effect estimates and confidence intervals are calculated if a group sequential design or an inverse normal combination test design was chosen, i.e., it is not applicable for Fisher's p-value combination test design. For the inverse normal combination test design with more than two stages, a warning informs that the validity of the confidence interval is theoretically shown only if no sample size change was performed.
A final stage p-value for Fisher's combination test is calculated only if a two-stage design was chosen. For Fisher's combination test, the conditional power for more than one remaining stages is estimated via simulation.
Final stage p-values, median unbiased effect estimates, and final confidence intervals are not calculated for multi-arm and enrichment designs.
Returns an AnalysisResults 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 analysis functions:
getClosedCombinationTestResults(),
getClosedConditionalDunnettTestResults(),
getConditionalPower(),
getConditionalRejectionProbabilities(),
getFinalConfidenceInterval(),
getFinalPValue(),
getRepeatedConfidenceIntervals(),
getRepeatedPValues(),
getStageResults(),
getTestActions()
## Not run:
# Example 1 One-Sample t 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(
cumulativeEvents = c(33, 72, 116),
cumulativeLogRanks = 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)
if (require(ggplot2)) plot(x, thetaRange = c(0, 0.8))
design <- getDesignConditionalDunnett(secondStageConditioning = FALSE)
y <- getAnalysisResults(design,
dataInput = data,
nPlanned = 40, thetaH1 = c(0.5, NA, 0.8), assumedStDevs = 1.2, stage = 1
)
summary(y)
if (require(ggplot2)) 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)
if (require(ggplot2)) plot(x, type = 2)
## End(Not run)