Class for Simulation Results Enrichment Means — SimulationResultsEnrichmentMeans • rpact

A class for simulation results means in enrichment designs.

Details

Use getSimulationEnrichmentMeans() to create an object of this type.

Fields

adaptations: Indicates whether or not an adaptation takes place at interim k. Is a logical vector of length kMax minus 1.
allocationRatioPlanned: The planned allocation ratio (n1 / n2) for the groups. For multi-arm designs, it is the allocation ratio relating the active arm(s) to the control. Is a single positive numeric value.
calcSubjectsFunction: An optional function that can be entered to define how sample size is recalculated. By default, recalculation is performed with conditional power with specified minNumberOfSubjectsPerStage and maxNumberOfSubjectsPerStage.
conditionalPower: The conditional power at each stage of the trial. Is a single numeric value between 0 and 1.
conditionalPowerAchieved: The calculated conditional power, under the assumption of observed or assumed effect sizes. Is a numeric matrix.
earlyStop: The probability to stopping the trial either for efficacy or futility. Is a numeric vector.
effectList: The list of subsets, prevalences and effect sizes with columns and number of rows reflecting the different situations to be considered.
effectMeasure: Criterion for treatment arm/population selection, either based on test statistic ("testStatistic") or effect estimate ("effectEstimate"). Is a single character value.
epsilonValue: Needs to be specified if typeOfSelection = "epsilon". Is a single numeric value.
expectedNumberOfSubjects: The expected number of subjects under specified alternative.
futilityPerStage: The per-stage probabilities of stopping the trial for futility. Is a numeric matrix.
futilityStop: In simulation results data set: indicates whether trial is stopped for futility or not.
intersectionTest: The multiple test used for intersection hypotheses in closed systems of hypotheses. Is a single character value.
iterations: The number of iterations used for simulations. Is a single numeric value representing a whole number.
maxNumberOfIterations: The number of simulation iterations. Is a single numeric value representing a whole number.
maxNumberOfSubjectsPerStage: Determines the maximum number of subjects per stage for data-driven sample size recalculation. For two treatment arms, is the number of subjects for both treatment arms. For multi-arm designs, is the minimum number of subjects per selected active arm. Is a numeric vector of length kMax containing whole numbers.
minNumberOfSubjectsPerStage: Determines the minimum number of subjects per stage for data-driven sample size recalculation. For two treatment arms, is the number of subjects for both treatment arms. For multi-arm designs, is the minimum number of subjects per selected active arm. Is a numeric vector of length kMax containing whole numbers.
numberOfPopulations: The number of populations in an enrichment design. Is a numeric matrix.
plannedSubjects: Determines the number of cumulated (overall) subjects when the interim stages are planned. For two treatment arms, is the number of subjects for both treatment arms. For multi-arm designs, refers to the number of subjects per selected active arm. Is a numeric vector of length kMax containing whole numbers.
populations: The number of populations in an enrichment design. Is a single numeric value representing a whole number.
rejectAtLeastOne: The probability to reject at least one of the (multiple) hypotheses. Is a numeric vector.
rejectedPopulationsPerStage: The simulated number of rejected populations per stage.
rValue: Needs to be specified if typeOfSelection = "rBest". Is a single numeric value.
sampleSizes: The sample sizes for each group and stage. Is a numeric vector of length number of stages times number of groups containing whole numbers.
seed: The seed used for random number generation. Is a single numeric value.
selectedPopulations: The selected populations in enrichment designs.
selectPopulationsFunction: An optional function that can be entered to define the way of how populations are selected.
stDev: The standard deviation used for sample size and power calculation. Is a single numeric value.
stDevH1: The standard deviation under which the conditional power or sample size recalculation is performed. Is a single numeric value.
stratifiedAnalysis: For enrichment designs, typically a stratified analysis should be chosen. When testing means and rates, a non-stratified analysis can be performed on overall data. For survival data, only a stratified analysis is possible. Is a single logical value.
successCriterion: Defines when the study is stopped for efficacy at interim. "all" stops the trial if the efficacy criterion has been fulfilled for all selected treatment arms/populations, "atLeastOne" stops if at least one of the selected treatment arms/populations is shown to be superior to control at interim. Is a single character value.
successPerStage: The simulated success probabilities per stage where success is defined by user. Is a numeric matrix.
thetaH1: The assumed effect under the alternative hypothesis. For survival designs, refers to the hazard ratio. Is a numeric vector.
threshold: The selection criterion: treatment arm/population is only selected if effectMeasure exceeds threshold. Either a single numeric value or a numeric vector of length activeArms referring to a separate threshold condition for each treatment arm.
typeOfSelection: The way the treatment arms or populations are selected at interim. Is a single character value.