rpact

name	rpact
description	Guide users through confirmatory adaptive clinical trial design and analysis using the rpact R package. Use this skill when the user asks about: adaptive designs, sample size reassessment, conditional power, inverse normal combination test, Fisher combination test, multi-stage designs, or rpact design objects.

Adaptive Clinical Trial Design with rpact

Note: This skill targets rpact >= 4.4.0 (CRAN). Dev version 4.4.0.9305 is on GitHub.

API reference

• Full function docs: references/llms.txt (source: https://docs.rpact.org/reference/)
• Workflow patterns: references/code_patterns.md

Key functions

Trial design

• getDesignGroupSequential() - Group sequential design (O'Brien-Fleming, Pocock, alpha-spending, etc.)
• getDesignInverseNormal() - Inverse normal combination test for adaptive designs
• getDesignFisher() - Fisher combination test for adaptive designs
• getDesignConditionalDunnett() - Conditional Dunnett test for multi-arm
• getDesignCharacteristics() - Design characteristics and properties
• getDesignSet() - Compare multiple designs
• getFutilityBounds() - Convert futility bounds between scales (z-value, p-value, conditional power, predictive power, reverse conditional power, effect estimate) (new in 4.3.0)

Sample size

• getSampleSizeMeans() - Continuous endpoints
• getSampleSizeRates() - Binary endpoints
• getSampleSizeSurvival() - Time-to-event endpoints
• getSampleSizeCounts() - Count data endpoints

Power

• getPowerMeans() - Continuous endpoints
• getPowerRates() - Binary endpoints
• getPowerSurvival() - Time-to-event endpoints
• getPowerCounts() - Count data endpoints
• getPowerAndAverageSampleNumber() - Power and ASN

Simulation

• getSimulationMeans() / getSimulationRates() / getSimulationSurvival() / getSimulationCounts() - Two-arm
• getSimulationMultiArmMeans() / getSimulationMultiArmRates() / getSimulationMultiArmSurvival() - Multi-arm
• getSimulationEnrichmentMeans() / getSimulationEnrichmentRates() / getSimulationEnrichmentSurvival() - Enrichment

Analysis

• getDataset() - Enter stage-wise observed data
• getStageResults() - Compute stage-wise test statistics
• getAnalysisResults() - Final adaptive analysis results
• getClosedCombinationTestResults() - Closed testing for multi-arm
• getClosedConditionalDunnettTestResults() - Dunnett closed testing
• getConditionalPower() - Conditional power at interim
• getConditionalRejectionProbabilities() - CRP for adaptive recalculation

Inference

• getFinalPValue() - Adjusted final p-value
• getFinalConfidenceInterval() - Adjusted confidence interval
• getRepeatedPValues() - Repeated p-values across stages
• getRepeatedConfidenceIntervals() - Repeated CIs across stages

Utilities

• getAccrualTime() - Define accrual period
• getPiecewiseSurvivalTime() - Piecewise exponential survival
• getEventProbabilities() - Event probability computation
• getNumberOfSubjects() - Expected accrual over time
• getObservedInformationRates() - Observed information rates
• getTestActions() - Decision at each stage
• getPerformanceScore() - Design performance evaluation

Plotting

• plot() methods for all design, power, simulation, and analysis objects
• getAvailablePlotTypes() - List available plot types for an object

I/O

• readDataset() / readDatasets() - Read data from CSV
• writeDataset() / writeDatasets() - Write data to CSV
• getObjectRCode() - Generate reproducible R code for any rpact object

Workflow patterns

For detailed code templates, read references/code_patterns.md.

Topics covered:

• Group sequential designs (OF, Pocock, alpha-spending, HSD, custom futility)
• Adaptive designs (inverse normal combination, Fisher combination)
• Sample size for survival (HR, medians, event probabilities, piecewise)
• Sample size for means and rates (one-sample, two-sample, risk ratio)
• Power computation (survival, means, rates)
• Simulation for survival (two-arm, piecewise/NPH, multiple scenarios)
• Multi-arm designs and simulation (Dunnett, arm selection, dose-response)
• Enrichment designs (subgroup-specific effects)
• Entering observed data with getDataset() (survival, means, rates)
• Stage results and analysis (getStageResults(), getAnalysisResults(), getTestActions())
• Conditional power and sample size reassessment
• Adjusted inference (final p-values, CIs, repeated p-values/CIs)
• Piecewise survival and accrual specification
• Comparing designs with getDesignSet()
• Plotting (boundaries, power curves, simulation results)
• Reproducible code with getObjectRCode()

Important design considerations

• typeOfDesign choices: "OF" (O'Brien-Fleming), "P" (Pocock), "asOF"/"asHSD"/"asKD" (alpha-spending variants). Alpha-spending versions ("as*") allow unequal information rate spacing
• getDesignInverseNormal() vs getDesignGroupSequential(): Same signature, but inverse normal is for adaptive designs where sample size can be recalculated between stages
• bindingFutility = FALSE: Non-binding futility is standard for confirmatory trials; efficacy bounds computed ignoring futility
• Survival parameterization: Can specify treatment effect via hazardRatio, median1/median2, lambda1/lambda2, or pi1/pi2 (event probabilities) — use only one
• plannedEvents in simulation: Vector of cumulative event counts at each analysis (not information fractions)
• Multi-arm intersectionTest: "Dunnett" (parametric, most powerful), "Simes" (non-parametric), "Bonferroni" (conservative)
• typeOfSelection: Controls arm selection at interim — "best" (highest effect), "rBest" (r best arms), "epsilon" (within epsilon of best), "all" (no dropping)
• getObjectRCode(): Use to generate reproducible R code for any rpact object — invaluable for reporting and validation
• futilityBoundsScale (v4.3.0+): In getDesignInverseNormal() and getDesignGroupSequential(), specify futility bounds on alternative scales (p-value, conditional power, etc.) that are internally converted
• alpha0Scale (v4.3.0+): In getDesignFisher(), specify alpha0 bounds on alternative scales
• efficacyStops / futilityStops (v4.2.1+): Control which stages have efficacy/futility stopping
• Dose-response (v4.2.0+): Multi-arm simulations support doseLevels for linear or sigmoid Emax models
• Unequal variances (v4.2.0+): getSampleSizeMeans(), getPowerMeans(), getSimulationMeans() support different variances per group
• rpact defaults: alpha = 0.05, beta = 0.2, kMax = NA (inferred), sided = 1 — always set explicitly for clinical trial designs