# NOT RUN {
# Run with default values
getDesignGroupSequential()
# The output is:
#
# Design parameters and output of group sequential design:
#
# User defined parameters: not available
#
# Derived from user defined parameters: not available
#
# Default parameters:
# Type of design : OF
# Maximum number of stages : 3
# Stages : 1, 2, 3
# Information rates : 0.333, 0.667, 1.000
# Significance level : 0.0250
# Type II error rate : 0.2
# Two-sided power : FALSE
# Delta for Wang & Tsiatis Delta class : 0
# Futility bounds (non-binding) : -Inf, -Inf
# Binding futility : FALSE
# Haybittle Peto constants : 3.000
# Parameter for alpha spending function : 1
# Parameter for beta spending function : 1
# Optimization criterion for optimum design within Wang & Tsiatis class : ASNH1
# Test : one-sided
# Tolerance : 1e-08
# Type of beta spending : none
#
# Output:
# Cumulative alpha spending : 0.0002592, 0.0071601, 0.0250000
# Critical values : 3.471, 2.454, 2.004
# Stage levels : 0.0002592, 0.0070554, 0.0225331
#
# Calculate the Pocock type alpha spending critical values if the second
# interim analysis was performed after 70% of information was observed
getDesignGroupSequential(informationRates = c(0.4, 0.7),
typeOfDesign = "asP")
# The output is:
#
# Design parameters and output of group sequential design :
# User defined parameters:
# Type of design : asP
# Stages : 1, 2
# Information rates : 0.400, 0.700
#
# Derived from user defined parameters :
# Maximum number of stages : 2
# Futility bounds (non-binding) : -Inf
#
# Default parameters:
# Significance level : 0.0250
# Type II error rate : 0.2
# Delta for Wang & Tsiatis Delta class : 0
# Parameter for alpha spending function : 1
# Parameter for beta spending function : 1
# Optimization criterion for Optimum design within Wang & Tsiatis class : ASNH1
# Test : one-sided
# Tolerance : 1e-08
# Type of beta : none
# Output:
# Cumulative alpha spending : 0.01308, 0.01974
# Critical values : 2.224, 2.305
# Stage levels : 0.01308, 0.01058
# }
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