getDesignGroupSequential {rpact} | R Documentation |

Provides adjusted boundaries and defines a group sequential design.

getDesignGroupSequential(..., kMax = NA_integer_, alpha = NA_real_, beta = NA_real_, sided = 1, informationRates = NA_real_, futilityBounds = NA_real_, typeOfDesign = C_DEFAULT_TYPE_OF_DESIGN, deltaWT = 0, optimizationCriterion = C_OPTIMIZATION_CRITERION_DEFAULT, gammaA = 1, typeBetaSpending = C_TYPE_OF_DESIGN_BS_NONE, userAlphaSpending = NA_real_, userBetaSpending = NA_real_, gammaB = 1, bindingFutility = C_BINDING_FUTILITY_DEFAULT, constantBoundsHP = C_CONST_BOUND_HP_DEFAULT, twoSidedPower = C_TWO_SIDED_POWER_DEFAULT, tolerance = C_DESIGN_TOLERANCE_DEFAULT)

`...` |
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 = 1, 2, ..., 10, default is 3. |

`alpha` |
The significance level alpha, default is 0.025. |

`beta` |
Type II error rate, necessary for providing sample size calculations |

`sided` |
One-sided or two-sided, default is 1. |

`informationRates` |
The information rates, default is |

`futilityBounds` |
The futility bounds (vector of length K - 1). |

`typeOfDesign` |
The type of design. Type of design is one of the following: O'Brien & Fleming ("OF"), Pocock ("P"), Wang & Tsiatis Delta class ("WT"), Haybittle & Peto ("HP"), Optimum design within Wang & Tsiatis class ("WToptimum"), O'Brien & Fleming type alpha spending ("OF"), Pocock type alpha spending ("asP"), Kim & DeMets alpha spending ("asKD"), Hwang, Shi & DeCani alpha spending ("asHSD"), user defined alpha spending ("asUser"), default is "OF". |

`deltaWT` |
Delta for Wang & Tsiatis Delta class. |

`optimizationCriterion` |
Optimization criterion for optimum design within Wang & Tsiatis class ("ASNH1", "ASNIFH1", "ASNsum"), default is "ASNH1". |

`gammaA` |
Parameter for alpha spending function, default is 1. |

`typeBetaSpending` |
Type of beta spending. Type of of beta spending is one of the following: O'Brien & Fleming type beta spending, Pocock type beta spending, Kim & DeMets beta spending, Hwang, Shi & DeCani beta spending, user defined beta spending ("bsOF", "bsP",...). |

`userAlphaSpending` |
The user defined alpha spending. |

`userBetaSpending` |
The user defined beta spending. |

`gammaB` |
Parameter for beta spending function, default is 1. |

`bindingFutility` |
If |

`constantBoundsHP` |
The constant bounds up to stage K - 1 for the Haybittle & Peto design (default is 3). |

`twoSidedPower` |
For two-sided testing, if |

`tolerance` |
The tolerance, default is 1e-08. |

Depending on `typeOfDesign`

some parameters are specified, others not.
For example, only if `typeOfDesign`

"asHSD" is selected, `gammaA`

needs to be specified.

If an alpha spending approach was specified ("asOF", "asP", "asKD", "asHSD", or "asUser") additionally a beta spending function can be specified to produce futility bounds.

Returns a `TrialDesignGroupSequential`

object.

`getDesignSet`

for creating a set of designs to compare.

# 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