Group Sequential Design

Description

Trial design for group sequential design.

Details

This object should not be created directly; use getDesignGroupSequential() with suitable arguments to create a group sequential design.

Fields

kMax

The maximum number of stages K. Is a numeric vector of length 1 containing a whole number.

alpha

The significance level alpha, default is 0.025. Is a numeric vector of length 1 containing a value between 0 and 1.

stages

The stage numbers of the trial. Is a numeric vector of length kMax containing whole numbers.

informationRates

The information rates (that must be fixed prior to the trial), default is (1:kMax) / kMax. Is a numeric vector of length kMax containing values between 0 and 1.

userAlphaSpending

The user defined alpha spending. Contains the cumulative alpha-spending (type I error rate) up to each interim stage. Is a numeric vector of length kMax containing values between 0 and 1.

criticalValues

The critical values for each stage of the trial. Is a numeric vector of length kMax.

stageLevels

The adjusted significance levels to reach significance in a group sequential design. Is a numeric vector of length kMax containing values between 0 and 1.

alphaSpent

The cumulative alpha spent at each stage. Is a numeric vector of length kMax containing values between 0 and 1.

bindingFutility

If TRUE, the calculation of the critical values is affected by the futility bounds and the futility threshold is binding in the sense that the study must be stopped if the futility condition was reached (default is FALSE) Is a logical vector of length 1.

tolerance

The numerical tolerance, default is 1e-06. Is a numeric vector of length 1.

typeOfDesign

The type of design. Is a character vector of length 1.

beta

The Type II error rate necessary for providing sample size calculations (e.g., in getSampleSizeMeans), beta spending function designs, or optimum designs, default is 0.20. Is a numeric vector of length 1 containing a value between 0 and 1.

deltaWT

Delta for Wang & Tsiatis Delta class. Is a numeric vector of length 1.

deltaPT1

Delta1 for Pampallona & Tsiatis class rejecting H0 boundaries. Is a numeric vector of length 1.

deltaPT0

Delta0 for Pampallona & Tsiatis class rejecting H1 (accepting H0) boundaries. Is a numeric vector of length 1.

futilityBounds

The futility bounds for each stage of the trial. Is a numeric vector of length kMax.

gammaA

The parameter for the alpha spending function. Is a numeric vector of length 1.

gammaB

The parameter for the beta spending function. Is a numeric vector of length 1.

optimizationCriterion

The optimization criterion for optimum design within the Wang & Tsiatis class ("ASNH1", "ASNIFH1", "ASNsum"), default is "ASNH1".

sided

Describes if the alternative is one-sided (1) or two-sided (2). Is a numeric vector of length 1 containing a whole number.

betaSpent

The cumulative beta level spent at each stage of the trial. Only applicable for beta-spending designs. Is a numeric vector of length kMax containing values between 0 and 1.

typeBetaSpending

The type of beta spending. Is a character vector of length 1.

userBetaSpending

The user defined beta spending. Contains the cumulative beta-spending up to each interim stage. Is a numeric vector of length kMax containing values between 0 and 1.

power

The one-sided power at each stage of the trial. Is a numeric vector of length kMax containing values between 0 and 1.

twoSidedPower

Specifies if power is defined two-sided at each stage of the trial. Is a logical vector of length 1.

constantBoundsHP

The constant bounds up to stage kMax - 1 for the Haybittle & Peto design (default is 3). Is a numeric vector of length 1.

betaAdjustment

If TRUE, beta spending values are linearly adjusted if an overlapping of decision regions for futility stopping at earlier stages occurs. Only applicable for two-sided beta-spending designs. Is a logical vector of length 1.

delayedInformation

Delay of information for delayed response designs. Is a numeric vector of length kMax minus 1 containing values between 0 and 1.

decisionCriticalValues

The decision critical values for each stage of the trial in a delayed response design. Is a numeric vector of length kMax.

reversalProbabilities

The probability to switch from stopping the trial for success (or futility) and reaching non-rejection (or rejection) in a delayed response design. Is a numeric vector of length kMax minus 1 containing values between 0 and 1.

See Also

getDesignGroupSequential() for creating a group sequential design.