Calculates and returns the analysis results for the specified design and data.
getAnalysisResults(
design,
dataInput,
...,
directionUpper = TRUE,
thetaH0 = NA_real_,
nPlanned = NA_real_,
allocationRatioPlanned = 1,
stage = NA_integer_,
maxInformation = NULL,
informationEpsilon = NULL
)The trial design.
The summary data used for calculating the test results.
This is either an element of DatasetMeans, of DatasetRates, or of DatasetSurvival
and should be created with the function getDataset.
For more information see getDataset.
Further arguments to be passed to methods (cf. separate functions in "See Also" below), e.g.,
thetaH1 and assumedStDev or pi1, pi2The
assumed effect size or assumed rates to calculate the
conditional power. Depending on the type of dataset, either thetaH1 (means and survival)
or pi1, pi2 (rates) can be specified.
For testing means, an assumed standard deviation can be specified, default is 1.
normalApproximationThe type of computation of the p-values. Default is FALSE for
testing means (i.e., the t test is used) and TRUE for testing rates and the hazard ratio.
For testing rates, if normalApproximation = FALSE is specified, the binomial test
(one sample) or the exact test of Fisher (two samples) is used for calculating the p-values.
In the survival setting, normalApproximation = FALSE has no effect.
equalVariancesThe type of t test. For testing means in two treatment groups, either
the t test assuming that the variances are equal or the t test without assuming this,
i.e., the test of Welch-Satterthwaite is calculated, default is TRUE.
iterationsIterations for simulating the power for Fisher's combination test.
If the power for more than one remaining stages is to be determined for
Fisher's combination test, it is estimated via simulation with specified
iterations, the default is 1000.
seedSeed for simulating the power for Fisher's combination test. See above, default is a random seed.
intersectionTestDefines the multiple test for the intersection
hypotheses in the closed system of hypotheses when testing multiple hypotheses.
Five options are available in multi-arm designs: "Dunnett", "Bonferroni", "Simes",
"Sidak", and "Hierarchical", default is "Dunnett".
Four options are available in population enrichment designs: "SpiessensDebois" (one subset only),
"Bonferroni", "Simes", and "Sidak", default is "Simes".
varianceOptionDefines the way to calculate the variance in multiple treatment arms (> 2)
or population enrichment designs for testing means. For multiple arms, three options are available:
"overallPooled", "pairwisePooled", and "notPooled", default is "overallPooled".
For enrichment designs, the options are: "pooled", "pooledFromFull" (one subset only),
and "notPooled", default is "pooled".
thetaH1 and assumedStDevs or piTreatments, piControlThe
assumed effect size or assumed rates to calculate the conditional power in multi-arm trials
or enrichment designs. For survival designs, thetaH1 refers to the hazard ratio.
You can specify a value or a vector with elements referring to the
treatment arms or the sub-populations, respectively. If not specified, the conditional
power is calculated under the assumption of observed effect sizes, standard deviations, rates, or hazard ratios.
stratifiedAnalysisFor enrichment designs, typically a stratified analysis should be chosen.
For testing means and rates, also a non-stratified analysis based on overall data can be performed.
For survival data, only a stratified analysis is possible (see Brannath et al., 2009), default is TRUE.
Specifies the direction of the alternative,
only applicable for one-sided testing; default is TRUE
which means that larger values of the test statistics yield smaller p-values.
The null hypothesis value,
default is 0 for the normal and the binary case (testing means and rates, respectively),
it is 1 for the survival case (testing the hazard ratio).
For non-inferiority designs, thetaH0 is the non-inferiority bound.
That is, in case of (one-sided) testing of
means: a value != 0
(or a value != 1 for testing the mean ratio) can be specified.
rates: a value != 0
(or a value != 1 for testing the risk ratio pi1 / pi2) can be specified.
survival data: a bound for testing H0: hazard ratio = thetaH0 != 1 can be specified.
For testing a rate in one sample, a value thetaH0 in (0, 1) has to be specified for
defining the null hypothesis H0: pi = thetaH0.
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.
The planned allocation ratio n1 / n2 for a two treatment groups
design, default is 1. For multi-arm designs, it is the allocation ratio relating the active arm(s) to the control.
The stage number (optional). Default: total number of existing stages in the data input.
Positive integer value specifying the maximum information.
Positive integer value specifying the 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 maxInformation, default is 0.
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,
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.
If intersectionTest = "Dunnett" or intersectionTest = "SpiessensDebois", or the design is a conditional Dunnett design and
the dataset is a multi-arm or enrichment dataset, rpact uses the R package mnormt
to calculate the analysis results.
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 only if effect size and sample size is specified. 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.
getObservedInformationRates for recalculation the observed information rates.
Other analysis functions:
getClosedCombinationTestResults(),
getClosedConditionalDunnettTestResults(),
getConditionalPower(),
getConditionalRejectionProbabilities(),
getFinalConfidenceInterval(),
getFinalPValue(),
getRepeatedConfidenceIntervals(),
getRepeatedPValues(),
getStageResults(),
getTestActions()
# NOT RUN {
# 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)
# }
# NOT RUN {
# }
Run the code above in your browser using DataLab