rpact (version 3.3.4)

TrialDesignInverseNormal: Inverse Normal Design

Description

Trial design for inverse normal method.

Fields

kMax

The maximum number of stages K. Is a positive integer of length 1 (default value is 3). The maximum selectable kMax is 20 for group sequential or inverse normal and 6 for Fisher combination test designs.

alpha

The significance level alpha, default is 0.025. Is a positive numeric of length 1.

stages

The stage numbers of the trial. Is an integer vector of length kMax.

informationRates

The information rates (that must be fixed prior to the trial), default is (1:kMax) / kMax. Is a numeric vector of length kMax (the maximum number of stages).

userAlphaSpending

The user defined alpha spending. Numeric vector of length kMax containing the cumulative alpha-spending (Type I error rate) up to each interim stage: 0 <= alpha_1 <= ... <= alpha_K <= alpha.

criticalValues

The critical values for each stage of the trial. Is a numeric vector of length kMax (the maximum number of stages).

stageLevels

The levels for each stage.

alphaSpent

The cumulative alpha spent at each stage. Is a numeric vector with length kMax (the maximum number of stages).

bindingFutility

Logical. If bindingFutility = TRUE is specified 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).

tolerance

The numerical tolerance, default is 1e-06. Is a positive numeric of length 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"), Pampallona & Tsiatis ("PT"), Haybittle & Peto ("HP"), Optimum design within Wang & Tsiatis class ("WToptimum"), O'Brien & Fleming type alpha spending ("asOF"), Pocock type alpha spending ("asP"), Kim & DeMets alpha spending ("asKD"), Hwang, Shi & DeCani alpha spending ("asHSD"), user defined alpha spending ("asUser"), no early efficacy stop ("noEarlyEfficacy"), default is "OF".

beta

Type II error rate, necessary for providing sample size calculations
(e.g., getSampleSizeMeans()), beta spending function designs, or optimum designs, default is 0.20. Is a positive numeric of length 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 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 - 1, where kMax is the maximum number of stages.

gammaA

Parameter for alpha spending function. Is a numeric vector of length 1.

gammaB

Parameter for beta spending function. Is a numeric vector of length 1.

optimizationCriterion

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

sided

Is the alternative one-sided (1) or two-sided (2), default is 1. Is a positive integer of length 1.

betaSpent

The cumulative beta level spent at each stage of the trial. For beta-spending designs, is a numeric vector of length kMax (the maximum number of stages).

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", "bsKD", "bsHSD", "bsUser", default is "none").

userBetaSpending

The user defined beta spending. Vector of length kMax containing the cumulative beta-spending up to each interim stage.

power

The one-sided power at each stage of the trial. Is a numeric vector of length kMax (the maximum number of stages).

twoSidedPower

Two-sided power at each stage of the trial. Is a numeric vector of length kMax (the maximum number of stages).

constantBoundsHP

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

betaAdjustment

Logical. 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.

delayedInformation

Delay of information for delayed response designs. Is a numeric value or a numeric vector of length kMax - 1.

decisionCriticalValues

TODO

reversalProbabilities

TODO

Details

This object should not be created directly; use getDesignInverseNormal() with suitable arguments to create a inverse normal design.

See Also

getDesignInverseNormal() for creating a inverse normal design.