getObservedInformationRates {rpact}

R Documentation

Get Observed Information Rates

Description

Recalculates the observed information rates from the specified dataset.

Usage

getObservedInformationRates(
  dataInput,
  ...,
  maxInformation = NULL,
  informationEpsilon = NULL,
  stage = NA_integer_
)

Arguments

dataInput	The dataset for which the information rates shall be recalculated.
...	Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed.
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 maxInformation, default is 0. Alternatively, a floating-point number > 0 and < 1 can be specified to define a relative information epsilon.
stage	The stage number (optional). Default: total number of existing stages in the data input.

Details

For means and rates the maximum information is the maximum number of subjects or the relative proportion if informationEpsilon < 1; for survival data it is the maximum number of events or the relative proportion if informationEpsilon < 1.

Value

Returns a list that summarizes the observed information rates.

See Also

• getAnalysisResults() for using getObservedInformationRates() implicit,

• www.rpact.org/vignettes/planning/rpact_boundary_update_example

Examples

# Absolute information epsilon:
# decision rule 45 >= 46 - 1, i.e., under-running
data <- getDataset(
    overallN = c(22, 45),
    overallEvents = c(11, 28)
)
getObservedInformationRates(data,
    maxInformation = 46, informationEpsilon = 1
)

# Relative information epsilon:
# last information rate = 45/46 = 0.9783,
# is > 1 - 0.03 = 0.97, i.e., under-running
data <- getDataset(
    overallN = c(22, 45),
    overallEvents = c(11, 28)
)
getObservedInformationRates(data,
    maxInformation = 46, informationEpsilon = 0.03
)

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