getDataset {rpact} | R Documentation |
Creates a dataset object and returns it.
getDataset(..., floatingPointNumbersEnabled = FALSE)
... |
A |
floatingPointNumbersEnabled |
If |
The different dataset types DatasetMeans
, of DatasetRates
, or
DatasetSurvival
can be created as follows:
An element of DatasetMeans
for one sample is created by
getDataset(sampleSizes =, means =, stDevs =)
where
sampleSizes
, means
, stDevs
are vectors with stagewise sample sizes,
means and standard deviations of length given by the number of available stages.
An element of DatasetMeans
for two samples is created by
getDataset(sampleSizes1 =, sampleSizes2 =, means1 =, means2 =,
stDevs1 =, stDevs2 =)
where
sampleSizes1
, sampleSizes2
, means1
, means2
,
stDevs1
, stDevs2
are vectors with
stagewise sample sizes, means and standard deviations for the two treatment groups
of length given by the number of available stages.
An element of DatasetRates
for one sample is created by
getDataset(sampleSizes =, events =)
where sampleSizes
, events
are vectors
with stagewise sample sizes and events of length given by the number of available stages.
An element of DatasetRates
for two samples is created by
getDataset(sampleSizes1 =, sampleSizes2 =, events1 =, events2 =)
where
sampleSizes1
, sampleSizes2
, events1
, events2
are vectors with stagewise sample sizes
and events for the two treatment groups of length given by the number of available stages.
An element of DatasetSurvival
is created by
getDataset(events =, logRanks =, allocationRatios =)
where
events
, logRanks
, and allocation ratios
are the stagewise events,
(one-sided) logrank statistics, and allocation ratios.
An element of DatasetMeans
, DatasetRates
, and DatasetSurvival
for more than one comparison is created by adding subsequent digits to the variable names.
The system can analyze these data in a multi-arm many-to-one comparison setting where the
group with the highest index represents the control group.
Prefix overall[Capital case of first letter of variable name]...
for the variable
names enables entering the overall results and calculates stagewise statistics.
n
can be used in place of samplesizes
.
Note that in survival design usually the overall events and logrank test statistics are provided
in the output, so
getDataset(overallEvents=, overallLogRanks =, overallAllocationRatios =)
is the usual command for entering survival data. Note also that for overallLogranks
also the
z scores from a Cox regression can be used.
For multi-arm designs, the index refers to the considered comparison. For example,
getDataset(events1=c(13, 33), logRanks1 = c(1.23, 1.55), events2 = c(16, NA), logRanks2 = c(1.55, NA))
refers to the case where one active arm (1) is considered at both stages whereas active arm 2
was dropped at interim. Number of events and logrank statistics are entered for the corresponding
comparison to control (see Examples).
For enrichment designs, the comparison of two samples is provided for an unstratified
(sub-population wise) or stratified data input.
For unstratified (sub-population wise) data input the data sets are defined for the sub-populations
S1, S2, ..., F, where F refers to the full populations. Use of getDataset(S1 = , S2, ..., F = )
defines the data set to be used in getAnalysisResults
(see examples)
For stratified data input the data sets are defined for the strata S1, S12, S2, ..., R, where R
refers to the remainder of the strata such that the union of all sets is the full population.
Use of getDataset(S1 = , S12 = , S2, ..., R = )
defines the data set to be used in
getAnalysisResults
(see examples)
For survival data, for enrichment designs the log-rank statistics should be entered as stratified
log-rank statistics in order to provide strong control of Type I error rate. For stratified data input,
the variables to be specified in getDataset()
are events
, expectedEvents
,
varianceEvents
, and allocationRatios
or overallEvents
, overallExpectedEvents
,
overallVarianceEvents
, and overallAllocationRatios
. From this, (stratified) log-rank tests are
calculated.
Returns a Dataset
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
,
# Create a Dataset of Means (one group): datasetOfMeans <- getDataset( n = c(22, 11, 22, 11), means = c(1, 1.1, 1, 1), stDevs = c(1, 2, 2, 1.3) ) datasetOfMeans datasetOfMeans$show(showType = 2) datasetOfMeans <- getDataset( overallSampleSizes = c(22, 33, 55, 66), overallMeans = c(1.000, 1.033, 1.020, 1.017), overallStDevs = c(1.00, 1.38, 1.64, 1.58) ) datasetOfMeans datasetOfMeans$show(showType = 2) as.data.frame(datasetOfMeans) # Create a Dataset of Means (two groups): datasetOfMeans <- getDataset( n1 = c(22, 11, 22, 11), n2 = c(22, 13, 22, 13), means1 = c(1, 1.1, 1, 1), means2 = c(1.4, 1.5, 3, 2.5), stDevs1 = c(1, 2, 2, 1.3), stDevs2 = c(1, 2, 2, 1.3) ) datasetOfMeans datasetOfMeans <- getDataset( overallSampleSizes1 = c(22, 33, 55, 66), overallSampleSizes2 = c(22, 35, 57, 70), overallMeans1 = c(1, 1.033, 1.020, 1.017), overallMeans2 = c(1.4, 1.437, 2.040, 2.126), overallStDevs1 = c(1, 1.38, 1.64, 1.58), overallStDevs2 = c(1, 1.43, 1.82, 1.74) ) datasetOfMeans df <- data.frame( stages = 1:4, n1 = c(22, 11, 22, 11), n2 = c(22, 13, 22, 13), means1 = c(1, 1.1, 1, 1), means2 = c(1.4, 1.5, 3, 2.5), stDevs1 = c(1, 2, 2, 1.3), stDevs2 = c(1, 2, 2, 1.3) ) datasetOfMeans <- getDataset(df) datasetOfMeans # Create a Dataset of Means (three groups) where the comparison of # treatment arm 1 to control is dropped at the second interim stage: datasetOfMeans <- getDataset( overallN1 = c(22, 33, NA), overallN2 = c(20, 34, 56), overallN3 = c(22, 31, 52), overallMeans1 = c(1.64, 1.54, NA), overallMeans2 = c(1.7, 1.5, 1.77), overallMeans3 = c(2.5, 2.06, 2.99), overallStDevs1 = c(1.5, 1.9, NA), overallStDevs2 = c(1.3, 1.3, 1.1), overallStDevs3 = c(1, 1.3, 1.8)) datasetOfMeans # Create a Dataset of Rates (one group): datasetOfRates <- getDataset( n = c(8, 10, 9, 11), events = c(4, 5, 5, 6) ) datasetOfRates # Create a Dataset of Rates (two groups): datasetOfRates <- getDataset( n2 = c(8, 10, 9, 11), n1 = c(11, 13, 12, 13), events2 = c(3, 5, 5, 6), events1 = c(10, 10, 12, 12) ) datasetOfRates # Create a Dataset of Rates (three groups) where the comparison of # treatment arm 2 to control is dropped at the first interim stage: datasetOfRates <- getDataset( overallN1 = c(22, 33, 44), overallN2 = c(20, NA, NA), overallN3 = c(20, 34, 44), overallEvents1 = c(11, 14, 22), overallEvents2 = c(17, NA, NA), overallEvents3 = c(17, 19, 33)) datasetOfRates # Create a Survival Dataset datasetSurvival <- getDataset( overallEvents = c(8, 15, 19, 31), overallAllocationRatios = c(1, 1, 1, 2), overallLogRanks = c(1.52, 1.98, 1.99, 2.11) ) datasetSurvival # Create a Survival Dataset with four comparisons where treatment # arm 2 was dropped at the first interim stage, and treatment arm 4 # at the second. datasetSurvival <- getDataset( overallEvents1 = c(18, 45, 56), overallEvents2 = c(22, NA, NA), overallEvents3 = c(12, 41, 56), overallEvents4 = c(27, 56, NA), overallLogRanks1 = c(1.52, 1.98, 1.99), overallLogRanks2 = c(3.43, NA, NA), overallLogRanks3 = c(1.45, 1.67, 1.87), overallLogRanks4 = c(1.12, 1.33, NA) ) datasetSurvival # Enrichment: Stratified and unstratified data input # The following data are from one study. Only the first # (stratified) data input enables a stratified analysis. # Stratified data input S1 <- getDataset( sampleSize1 = c(18, 17), sampleSize2 = c(12, 33), mean1 = c(125.6, 111.1), mean2 = c(107.7, 77.7), stDev1 = c(120.1, 145.6), stDev2 = c(128.5, 133.3)) S2 <- getDataset( sampleSize1 = c(11, NA), sampleSize2 = c(14, NA), mean1 = c(100.1, NA), mean2 = c( 68.3, NA), stDev1 = c(116.8, NA), stDev2 = c(124.0, NA)) S12 <- getDataset( sampleSize1 = c(21, 17), sampleSize2 = c(21, 12), mean1 = c(135.9, 117.7), mean2 = c(84.9, 107.7), stDev1 = c(185.0, 92.3), stDev2 = c(139.5, 107.7)) R <- getDataset( sampleSize1 = c(19, NA), sampleSize2 = c(33, NA), mean1 = c(142.4, NA), mean2 = c(77.1, NA), stDev1 = c(120.6, NA), stDev2 = c(163.5, NA)) dataEnrichment <- getDataset(S1 = S1, S2 = S2, S12 = S12, R = R) dataEnrichment # Unstratified data input S1N <- getDataset( sampleSize1 = c(39, 34), sampleSize2 = c(33, 45), stDev1 = c(156.503, 120.084), stDev2 = c(134.025, 126.502), mean1 = c(131.146, 114.4), mean2 = c(93.191, 85.7)) S2N <- getDataset( sampleSize1 = c(32, NA), sampleSize2 = c(35, NA), stDev1 = c(163.645, NA), stDev2 = c(131.888, NA), mean1 = c(123.594, NA), mean2 = c(78.26, NA)) F <- getDataset( sampleSize1 = c(69, NA), sampleSize2 = c(80, NA), stDev1 = c(165.468, NA), stDev2 = c(143.979, NA), mean1 = c(129.296, NA), mean2 = c(82.187, NA)) dataEnrichmentN <- getDataset(S1 = S1N, S2 = S2N, F = F) dataEnrichmentN