getAnalysisResults {rpact}R Documentation

Get Analysis Results

Description

Calculates and returns the analysis results for the specified design and data.

Usage

getAnalysisResults(design, dataInput, ...,
  directionUpper = C_DIRECTION_UPPER_DEFAULT, thetaH0 = NA_real_,
  nPlanned = NA_real_)

Arguments

design

The trial design.

dataInput

The summary data used for calculating the test results. This is either an element of DatasetMeans, of DatasetRates, or of DatasetSurvival. For more information see details below.

...

Further arguments to be passed to methods (cp. separate functions in See Also), e.g.,

stage

The stage number (optional). Default: total number of existing stages in the data input.

allocationRatioPlanned

The allocation ratio n1/n2 for two treatment groups planned for the subsequent stages, the default value is 1.

thetaH1 and assumedStDev or pi1, pi2

The assumed effect size or assumed rates to calculate the conditional power. Depending on the type of dataset, either thetaH1 (means and survival) or pi1, pi2 (rates) can be specified. Additionally, if testing means is specified, an assumed standard deviation can be specified, default is 1.

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.

iterations

Iterations for simulating the power for Fisher's combination test. If the power for more than one remaining stages is to be determined for Fisher's combination test, it is estimated via simulation with specified
iterations, the default value is 10000.

seed

Seed for simulating the power for Fisher's combination test. See above, default is a random seed.

directionUpper

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

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 noninferiority designs, this is the noninferiority bound.

nPlanned

The sample size planned for the subsequent stages. It should be a vector with length equal to the remaining stages and is the overall sample size in the two treatment groups if two groups are considered.

Details

Given a design and a dataset, at given stage the function calculates the test results (effect sizes, stage-wise test statistics and p-values, overall p-values and test statistics, conditional rejection probability (CRP), conditional power, Repeated Confidence Intervals (RCIs), repeated overall p-values, and final stage p-values, median unbiased effect estimates, and final confidence intervals.

dataInput is either an element of DatasetMeans, of DatasetRates, or of DatasetSurvival and should be created with the function getDataset.

Value

Returns an AnalysisResults object.

Note

The conditional power is calculated only if effect size and sample size is specified. Median unbiased effect estimates and confidence intervals are calculated if a group sequential design or an inverse normal combination test design was chosen, i.e., it is not applicable for Fisher's p-value combination test design.

A final stage p-value for Fisher's combination test is calculated only if a two-stage design was chosen. For Fisher's combination test, the conditional power for more than one remaining stages is estimated via simulation.

See Also

Alternatively the analysis results can be calculated separately using one of the following functions:

Examples

design <- getDesignGroupSequential()
dataMeans <- getDataset(
    n = c(10,10),
    means = c(1.96,1.76),
    stDevs = c(1.92,2.01))

getAnalysisResults(design, dataMeans)

# produces:
#
# Analysis results (group sequential design):
#  Stages                        : 1, 2, 3 
#  Information rates             : 0.333, 0.667, 1.000 
#  Critical values               : 3.471, 2.454, 2.004 
#  Futility bounds (non-binding) : -Inf, -Inf 
#  Cumulative alpha spending     : 0.0002592, 0.0071601, 0.0250000 
#  Stage levels                  : 0.0002592, 0.0070554, 0.0225331 
#  Effect sizes                  : 1.96, 1.86, NA 
#  Test statistics               : 3.228, 2.769, NA 
#  p-values                      : 0.005177, 0.010895, NA 
#  Overall test statistics       : 3.228, 4.342, NA 
#  Overall p-values              : 0.0051766, 0.0001757, NA 
#  Futility bounds for power     : NA 
#  Actions                       : continue, reject and stop, NA 
#  Theta H0                      : 0 
#  CRP                           : 0.3177, 0.9434, NA 
#  Planned sample size           : NA, NA, NA 
#  Planned allocation ratio      : 1 
#  Assumed effect                : NA 
#  Assumed standard deviation    : 1 
#  Conditional power             : NA, NA, NA 
#  RCIs (lower)                  : -1.236, 0.702, NA 
#  RCIs (upper)                  : 5.16, 3.02, NA 
#  Repeated p-values             : 0.081766, 0.001825, NA 
#  Final stage                   : 2 
#  Final p-value                 : NA, 0.0004094, NA 
#  Final CIs (lower)             : NA, 0.654, NA 
#  Final CIs (upper)             : NA, 2.36, NA 
#  Median unbiased estimate      : NA, 1.51, NA 


[Package rpact version 1.0.0 Index | www.rpact.org]