Getting started with rpact

Vignettes

The vignettes are hosted at www.rpact.com/vignettes and cover the following topics:

1. Defining group-sequential boundaries
2. Designing group-sequential trials with two groups and a continuous endpoint
3. Designing group-sequential trials with a binary endpoint
4. Designing group-sequential trials with two groups and a survival endpoint
5. Simulation-based design of group-sequential trials with a survival endpoint
6. An example to illustrate boundary re-calculations during the trial
7. Analysis of a group-sequential trial with a survival endpoint
8. Defining accrual time and accrual intensity
9. How to use R generics with rpact
10. How to create admirable plots with rpact
11. Comparing sample size and power calculation results for a group-sequential trial with a survival endpoint: rpact vs. gsDesign
12. Supplementing and enhancing rpact’s graphical capabilities with ggplot2
13. Using the inverse normal combination test for analyzing a trial with continuous endpoint and potential sample size reassessment
14. Planning a trial with binary endpoints
15. Planning a survival trial
16. Simulation of a trial with a binary endpoint and unblinded sample size re-calculation
17. How to create summaries
18. How to create analysis result (one- and multi-arm) plots
19. How to create simulation result (one- and multi-arm) plots
20. Simulating multi-arm designs with a continuous endpoint
21. Analysis of a multi-arm design with a binary endpoint
22. Step-by-Step rpact Tutorial
23. Planning and Analyzing a Group-Sequential Multi-Arm-Multi-Stage Design with Binary Endpoint using rpact
24. Two-arm analysis for continuous data with covariates from raw data (exclusive)
25. How to install the latest developer version (exclusive)

Workflow

• Everything is starting with a design, e.g.: design <- getDesignGroupSequential()
• Find the optimal design parameters with help of rpact comparison tools: getDesignSet
• Calculate the required sample size, e.g.: getSampleSizeMeans(), getPowerMeans()
• Simulate specific characteristics of an adaptive design, e.g.: getSimulationMeans()
• Collect your data, import it into R and create a dataset: data <- getDataset()
• Analyze your data: getAnalysisResults(design, data)

Focus on Usability

The most important rpact functions have intuitive names:

• getDesign[GroupSequential/InverseNormal/Fisher]()
• getDesignCharacteristics()
• getSampleSize[Means/Rates/Survival]()
• getPower[Means/Rates/Survival]()
• getSimulation[MultiArm/Enrichment]`[Means/Rates/Survival]()`
• getDataSet()
• getAnalysisResults()
• getStageResults()

RStudio/Eclipse: auto code completion makes it easy to use these functions.

R generics

In general, everything runs with the R standard functions which are always present in R: so-called R generics, e.g., print, summary, plot, as.data.frame, names, length

Utilities

Several utility functions are available, e.g.

• getAccrualTime()
• getPiecewiseSurvivalTime()
• getNumberOfSubjects()
• getEventProbabilities()
• getPiecewiseExponentialDistribution()
• survival helper functions for conversion of pi, lambda and median, e.g., getLambdaByMedian()
• testPackage(): installation qualification on a client computer or company server (via unit tests)