| getPerformanceScore {rpact} | R Documentation |
Calculates the conditional performance score, its sub-scores and components according to (Herrmann et al. (2020), doi:10.1002/sim.8534) and (Bokelmann et al. (2024), doi:10.1186/s12874-024-02150-4) for a given simulation result from a two-stage design with continuous or binary endpoint. Larger (sub-)score and component values refer to a better performance.
getPerformanceScore(simulationResult)
simulationResult |
A simulation result. |
The conditional performance score consists of two sub-scores, one for the sample size (subscoreSampleSize) and one for the conditional power (subscoreConditionalPower). Each of those are composed of a location (locationSampleSize, locationConditionalPower) and variation component (variationSampleSize, variationConditionalPower). The term conditional refers to an evaluation perspective where the interim results suggest a trial continuation with a second stage. The score can take values between 0 and 1. More details on the performance score can be found in Herrmann et al. (2020), doi:10.1002/sim.8534 and Bokelmann et al. (2024) doi:10.1186/s12874-024-02150-4.
Stephen Schueuerhuis
## Not run:
# Example from Table 3 in "A new conditional performance score for
# the evaluation of adaptive group sequential designs with samplesize
# recalculation from Herrmann et al 2023", p. 2097 for
# Observed Conditional Power approach and Delta = 0.5
# Create two-stage Pocock design with binding futility boundary at 0
design <- getDesignGroupSequential(
kMax = 2, typeOfDesign = "P",
futilityBounds = 0, bindingFutility = TRUE)
# Initialize sample sizes and effect;
# Sample sizes are referring to overall stage-wise sample sizes
n1 <- 100
n2 <- 100
nMax <- n1 + n2
alternative <- 0.5
# Perform Simulation; nMax * 1.5 defines the maximum
# sample size for the additional stage
simulationResult <- getSimulationMeans(
design = design,
normalApproximation = TRUE,
thetaH0 = 0,
alternative = alternative,
plannedSubjects = c(n1, nMax),
minNumberOfSubjectsPerStage = c(NA_real_, 1),
maxNumberOfSubjectsPerStage = c(NA_real_, nMax * 1.5),
conditionalPower = 0.8,
directionUpper = TRUE,
maxNumberOfIterations = 1e05,
seed = 140
)
# Calculate performance score
getPerformanceScore(simulationResult)
## End(Not run)