SimulationResultsEnrichmentSurvival: Class for Simulation Results Enrichment Survival
In rpact: Confirmatory Adaptive Clinical Trial Design and Analysis

SimulationResultsEnrichmentSurvival

R Documentation

Class for Simulation Results Enrichment Survival

Description

A class for simulation results survival in enrichment designs.

Details

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

Fields

maxNumberOfIterations: The number of simulation iterations. Is a numeric vector of length 1 containing a whole number.
seed: The seed used for random number generation. Is a numeric vector of length 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 positive numeric vector of length 1.
conditionalPower: The conditional power at each stage of the trial. Is a numeric vector of length 1 containing a value between 0 and 1.
iterations: The number of iterations used for simulations. Is a numeric vector of length 1 containing a whole number.
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.
directionUpper: Specifies the direction of the alternative, only applicable for one-sided testing. Default is TRUE which means that larger values of the test statistics yield smaller p-values. Is a logical vector of length 1.
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.
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.
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.
thetaH1: The assumed effect under the alternative hypothesis. For survival designs, refers to the hazard ratio. Is a numeric vector.
calcEventsFunction: An optional function that can be entered to define how event size is recalculated. By default, recalculation is performed with conditional power with specified minNumberOfEventsPerStage and maxNumberOfEventsPerStage.
expectedNumberOfEvents: The expected number of events under specified alternative. Is a numeric vector.
populations: The number of populations in an enrichment design. Is a numeric vector of length 1 containing a whole number.
effectList: The list of subsets, prevalences and effect sizes with columns and number of rows reflecting the different situations to be considered.
intersectionTest: The multiple test used for intersection hypotheses in closed systems of hypotheses. Is a character vector of length 1.
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 logical vector of length 1.
adaptations: Indicates whether or not an adaptation takes place at interim k. Is a logical vector of length kMax minus 1.
typeOfSelection: The way the treatment arms or populations are selected at interim. Is a character vector of length 1.
effectMeasure: Criterion for treatment arm/population selection, either based on test statistic ("testStatistic") or effect estimate ("effectEstimate"). Is a character vector of length 1.
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 character vector of length 1.
epsilonValue: Needs to be specified if typeOfSelection = "epsilon". Is a numeric vector of length 1.
rValue: Needs to be specified if typeOfSelection = "rBest". Is a numeric vector of length 1.
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.
selectPopulationsFunction: An optional function that can be entered to define the way of how populations are selected.
correlationComputation: If "alternative", a correlation matrix according to Deng et al. (Biometrics, 2019) accounting for the respective alternative is used for simulating log-rank statistics in the many-to-one design. If "null", a constant correlation matrix valid under the null hypothesis is used.
earlyStop: The probability to stopping the trial either for efficacy or futility. Is a numeric vector.
selectedPopulations: The selected populations in enrichment designs.
numberOfPopulations: The number of populations in an enrichment design. Is a numeric matrix.
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.
successPerStage: The simulated success probabilities per stage where success is defined by user. Is a numeric matrix.
eventsPerStage: The number of events per stage. Is a numeric matrix.
singleNumberOfEventsPerStage: In simulation results data set: the number of events per stage that is used for the analysis.
conditionalPowerAchieved: The calculated conditional power, under the assumption of observed or assumed effect sizes. Is a numeric matrix.