rpact (version 2.0.6)

getFinalConfidenceInterval: Get Final Confidence Interval

Description

Returns the final confidence interval for the parameter of interest. It is based on the prototype case, i.e., the test for testing a mean for normally distributed variables.

Usage

getFinalConfidenceInterval(design, dataInput, ...)

Arguments

design

The trial design.

dataInput

The data input.

stage

The stage number.

thetaH0

The null hypothesis value, default is 0 for the normal and the binary case, it is 1 for the survival case. 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. For non-inferiority designs, this is the non-inferiority bound.

directionUpper

The direction of one-sided testing. Default is directionUpper = TRUE which means that larger values of the test statistics yield smaller p-values.

normalApproximation

The 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 test of Fisher (two samples) is used for calculating the p-values. In the survival setting normalApproximation = FALSE has no effect.

equalVariances

The 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 equalVariances = TRUE.

Value

Returns a list containing

• finalStage,

• medianUnbiased,

• finalConfidenceInterval,

• medianUnbiasedGeneral, and

• finalConfidenceIntervalGeneral.

Details

Depending on design and dataInput the final confidence interval and median unbiased estimate that is based on the stagewise ordering of the sample space will be calculated and returned. Additionally, a non-standardized ("general") version is provided, use the standard deviation to obtain the confidence interval for the parameter of interest.

Examples

# NOT RUN {
design <- getDesignInverseNormal(kMax = 2)
data <- getDataset(
    n = c(20, 30),
    means = c(50, 51),
    stDevs = c(130, 140)
)
getFinalConfidenceInterval(design, dataInput = data)

# Results in:
#
# $finalStage
# [1] 2
# 
# $medianUnbiasedGeneral
# [1] 0.3546145
# 
# $finalConfidenceIntervalGeneral
# [1] 0.06967801 0.63468553
# 
# $medianUnbiased
# [1] 47.7787
# 
# $finalConfidenceInterval
# [1]  9.388012 85.513851'

# }