getDesignFisher: Get Design Fisher

Description

Performs Fisher's combination test and returns critical values for this design.

Usage

getDesignFisher(..., kMax = NA_integer_, alpha = NA_real_,
  method = C_FISHER_METHOD_DEFAULT, userAlphaSpending = NA_real_,
  alpha0Vec = NA_real_, informationRates = NA_real_, sided = 1,
  bindingFutility = C_BINDING_FUTILITY_FISHER_DEFAULT,
  tolerance = C_ANALYSIS_TOLERANCE_FISHER_DEFAULT, iterations = 0,
  seed = NA_real_)

Arguments

...

Ensures that all arguments are be named and that a warning will be displayed if unknown arguments are passed.

kMax

The maximum number of stages K. K = 2, 3, ..., 6, default is 3.

alpha

The significance level alpha, default is 0.025.

method

"equalAlpha", "fullAlpha", "noInteraction", or "userDefinedAlpha", default is "equalAlpha".

userAlphaSpending

A vector of levels 0 < alpha_1 < ... < alpha_K < alpha specifying the cumulative Type I error rate.

alpha0Vec

Stopping for futility bounds for stage-wise p-values.

informationRates

Information rates that must be fixed prior to the trial, default is (1 : kMax) / kMax.

sided

Is the alternative one-sided (1) or two-sided (2), default is 1.

bindingFutility

If bindingFutility = FALSE is specified the calculation of the critical values is not affected by the futility bounds (default is TRUE).

tolerance

The tolerance, default is 1E-14.

iterations

The number of simulation iterations, e.g., getDesignFisher(iterations = 100000) checks the validity of the critical values for the default design. The default value of iterations is 0, i.e., no simulation will be executed.

seed

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

Value

Returns a TrialDesignFisher object

Details

getDesignFisher calculates the critical values and stage levels for Fisher's combination test as described in Bauer (1989), Bauer and Koehne (1994), Bauer and Roehmel (1995), and Wassmer (1999) for equally and unequally sized stages.

Examples

Run this code

# NOT RUN {
# Run with default values
getDesignFisher() 

# The output is:
#  
# Design parameters and output of Fisher design:
# User defined parameters: not available
# 
# Derived from user defined parameters: not available
# 
# Default parameters:
#   Method                          : equalAlpha 
#   Maximum number of stages        : 3 
#   Stages                          : 1, 2, 3 
#   Information rates               : 0.333, 0.667, 1.000 
#   Significance level              : 0.0250 
#   Alpha_0                         : 1.0000, 1.0000 
#   Binding futility                : TRUE 
#   Test                            : one-sided 
#   Tolerance                       : 1e-14 
# 
# Output:
#   Cumulative alpha spending       : 0.01231, 0.01962, 0.02500 
#   Critical values                 : 0.0123085, 0.0016636, 0.0002911 
#   Stage levels                    : 0.01231, 0.01231, 0.01231 
#   Scale                           : 1, 1 
#   Non stochastic curtailment      : FALSE 

# }