rpact: Confirmatory Adaptive Clinical Trial Design and Analysis


getDataset {rpact}R Documentation

Get Dataset

Description

Creates a dataset object and returns it.

Usage

getDataset(..., floatingPointNumbersEnabled = FALSE)

Arguments

...

A data.frame or some data vectors defining the dataset.

floatingPointNumbersEnabled

If TRUE, sample sizes can be specified as floating-point numbers (this make sense, e.g., for theoretical comparisons);
by default floatingPointNumbersEnabled = FALSE, i.e., samples sizes defined as floating-point numbers will be truncated.

Details

The different dataset types DatasetMeans, of DatasetRates, or DatasetSurvival can be created as follows:

Prefix overall[Capital case of first letter of variable name]... for the variable names enables entering the overall results and calculates stagewise statistics.

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).

n can be used in place of samplesizes.

Value

Returns a Dataset object. The following generics (R generic functions) are available for this result object:

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)

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



[Package rpact version 3.0.2 Index | www.rpact.org]