Inverse Normal Design

Trial design for inverse normal method.

Details

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

Fields

kMax: The maximum number of stages K. Is a single numeric value representing a whole number.
alpha: The significance level alpha, default is 0.025. Is a single numeric 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 single logical value.
tolerance: The numerical tolerance, default is 1e-06. Is a single numeric value.
typeOfDesign: The type of design. Is a single character value.
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 single numeric value between 0 and 1.
deltaWT: Delta for Wang & Tsiatis Delta class. Is a single numeric value.
deltaPT1: Delta1 for Pampallona & Tsiatis class rejecting H0 boundaries. Is a single numeric value.
deltaPT0: Delta0 for Pampallona & Tsiatis class rejecting H1 (accepting H0) boundaries. Is a single numeric value.
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 single numeric value.
gammaB: The parameter for the beta spending function. Is a single numeric value.
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 single numeric value representing 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 single character value.
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.
efficacyStops: Logical vector indicating efficacy stops Is a logical vector of length kMax minus 1.
futilityStops: Logical vector indicating futility stops Is a logical vector of length kMax minus 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 single logical value.
constantBoundsHP: The constant bounds up to stage kMax - 1 for the Haybittle & Peto design (default is 3). Is a single numeric value.
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 single logical value.
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.

Details

Fields

See also