getDataset: Get Dataset

In rpact: Confirmatory Adaptive Clinical Trial Design and Analysis

Get Dataset

Description

Creates a dataset object and returns it.

Usage

getDataset(..., floatingPointNumbersEnabled = FALSE)

getDataSet(..., floatingPointNumbersEnabled = FALSE)

Arguments

...	A data.frame or some data vectors defining the dataset.
floatingPointNumbersEnabled	If TRUE, sample sizes and event numbers can be specified as floating-point numbers (this make sense, e.g., for theoretical comparisons); by default floatingPointNumbersEnabled = FALSE, i.e., samples sizes and event numbers defined as floating-point numbers will be truncated.

Details

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 stage-wise 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 stage-wise 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 stage-wise 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 stage-wise 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 stage-wise 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 (cumulative) results and calculates stage-wise statistics. Since rpact version 3.2, the prefix cumulative[Capital case of first letter of variable name]... or cum[Capital case of first letter of variable name]... can alternatively be used for this.

n can be used in place of samplesizes.

Note that in survival design usually the overall (cumulative) events and logrank test statistics are provided in the output, so
getDataset(cumulativeEvents=, cumulativeLogRanks =, cumulativeAllocationRatios =)
is the usual command for entering survival data. Note also that for cumulativeLogranks 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.

Value

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,

• as.matrix() to coerce the object to a matrix.

Examples

# 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$show(showType = 2)
## Not run: 
datasetOfMeans <- getDataset(
    cumulativeSampleSizes = c(22, 33, 55, 66),
    cumulativeMeans = c(1.000, 1.033, 1.020, 1.017),
    cumulativeStDevs = c(1.00, 1.38, 1.64, 1.58)
)
datasetOfMeans
datasetOfMeans$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(
    cumulativeSampleSizes1 = c(22, 33, 55, 66),
    cumulativeSampleSizes2 = c(22, 35, 57, 70),
    cumulativeMeans1  = c(1, 1.033, 1.020, 1.017),
    cumulativeMeans2  = c(1.4, 1.437, 2.040, 2.126),
    cumulativeStDevs1 = c(1, 1.38, 1.64, 1.58),
    cumulativeStDevs2 = 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(
   cumN1      = c(22, 33, NA),
   cumN2      = c(20, 34, 56),
   cumN3      = c(22, 31, 52),
   cumMeans1  = c(1.64, 1.54, NA),
   cumMeans2  = c(1.7, 1.5, 1.77),
   cumMeans3  = c(2.5, 2.06, 2.99),
   cumStDevs1 = c(1.5, 1.9, NA),
   cumStDevs2 = c(1.3, 1.3, 1.1),
   cumStDevs3 = 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(
    cumN1      = c(22, 33, 44),
    cumN2      = c(20, NA, NA),
    cumN3      = c(20, 34, 44),
    cumEvents1 = c(11, 14, 22),
    cumEvents2 = c(17, NA, NA),
    cumEvents3 = c(17, 19, 33))
datasetOfRates

# Create a Survival Dataset
datasetSurvival <- getDataset(
    cumEvents = c(8, 15, 19, 31),
    cumAllocationRatios = c(1, 1, 1, 2),
    cumLogRanks = 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(
    cumEvents1   = c(18, 45, 56),
    cumEvents2   = c(22, NA, NA),
    cumEvents3   = c(12, 41, 56),
    cumEvents4   = c(27, 56, NA),
    cumLogRanks1 = c(1.52, 1.98, 1.99),
    cumLogRanks2 = c(3.43, NA, NA),
    cumLogRanks3 = c(1.45, 1.67, 1.87),
    cumLogRanks4 = 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

## End(Not run)

rpact documentation built on July 9, 2023, 6:30 p.m.