Designing Clinical Trials in R with rpact and crmPack

33rd International Biometric Conference, Seoul, South Korea

Daniel Sabanés Bové, Friedrich Pahlke

RPACT

July 13, 2026

Disclaimer

I am a Partner at RPACT GmbH (Germany, rpact.com) and a Director at inferential.biostatistics GmbH (Switzerland, inferential.bio).
I am a former employee of Roche (Switzerland, roche.com) and Google.

This talk: two examples of open-source software revolution!

Read our new Drug Discovery Today paper here (open access)

Clinical Trial Ecosystem in R 🌏

From: First R package for clinical trial design was likely S+SeqTrial
- Developed for S-PLUS by Emerson (2000)
- Available for free at rct-design.com
To: Clinical Trials CRAN task view
- Includes rpact and crmPack
- Also includes many other packages such as pwr, DoseFinding, gsDesign, bcrm, Mediana
As with other areas of R packages, the quality varies
- Professional support, testing and documentation are crucial for long term stability and value
- Comparison with Posit (formerly RStudio) supporting important general purpose packages like shiny, tidyverse, dplyr, ggplot2

rpact

Overview 📦

Comprehensive validated R package implementing methodology described in Wassmer and Brannath (2025)
Enables the design of traditional and confirmatory adaptive group sequential designs
Provides interim data analysis including early efficacy stopping and futility analyses
Enables sample-size reassessment with different strategies
Enables treatment arm selection in multi-stage multi-arm (MAMS) designs
Enables subset selection in population enrichment designs
Provides a comprehensive and reliable sample size calculator

Developed by RPACT 🏢

RPACT company founded in 2017 by Gernot Wassmer and Friedrich Pahlke
Idea: open source development with help of “crowd funding”
Currently supported by \(>\) 20 companies (more than half of top 20 pharma companies included)
\(>\) 80 presentations and training courses since 2018, e.g., FDA in March 2022
31 vignettes based on Quarto and published on rpact.org/vignettes
32 releases on CRAN since 2018
October 2025: Daniel Sabanés Bové joined RPACT as partner

Trial Designs 🔬

Fixed sample designs:
- continuous, binary, count, survival outcomes
Group sequential designs:
- efficacy interim analyses, futility stopping, alpha-spending functions
Adaptive designs:
- Inverse normal and Fisher’s combination test, conditional error rate principle
- Provides adjusted confidence intervals and bias corrected estimates
- Multi-arm multi-stage (MAMS) and enrichment designs, sample size reassessment

Easy to understand R commands:

getDesignGroupSequential()
getDesignInverseNormal()
getDesignFisher()
getDesignConditionalDunnett()

Sample Size and Power Calculation 💻

Sample size and power can be calulcated for testing:

means (continuous endpoint)
proportions (binary endpoint)
hazards (survival endpoint)
- Note: flexible recruitment and survival time options
rates (count endpoint)

Easy to understand R commands:

getSampleSize[Means / Rates / Survival / Counts]()
getPower[Means / Rates / Survival / Counts]()

Example: Sample Size Calculation 🧮

library(rpact)

# Define the design
mySampleSizeSummary <- getDesignGroupSequential(
    typeOfDesign = "asOF",
    futilityBounds = c(0, 0)
) |>
    # Perform sample size calculation
    getSampleSizeMeans(
        alternative = 2,
        stDev = 5
    ) |>
    # Obtain summary
    summary()

Example: Sample Size Calculation Output

Sample size calculation for a continuous endpoint

Sequential analysis with a maximum of 3 looks (group sequential design), one-sided overall significance level 2.5%, power 80%. The results were calculated for a two-sample t-test, H0: mu(1) - mu(2) = 0, H1: effect = 2, standard deviation = 5.

Stage	1	2	3
Planned information rate	33.3%	66.7%	100%
Cumulative alpha spent	0.0001	0.0060	0.0250
Stage levels (one-sided)	0.0001	0.0060	0.0231
Efficacy boundary (z-value scale)	3.710	2.511	1.993
Futility boundary (z-value scale)	0	0
Efficacy boundary (t)	4.690	2.152	1.384
Futility boundary (t)	0	0
Cumulative power	0.0204	0.4371	0.8000
Number of subjects	69.9	139.9	209.8
Expected number of subjects under H1			170.9
Overall exit probability (under H0)	0.5001	0.1309
Overall exit probability (under H1)	0.0684	0.4202
Exit probability for efficacy (under H0)	0.0001	0.0059
Exit probability for efficacy (under H1)	0.0204	0.4167
Exit probability for futility (under H0)	0.5000	0.1250
Exit probability for futility (under H1)	0.0480	0.0035

Legend:

(t): treatment effect scale

Example: Different Plots

plot(mySampleSizeSummary, type = 1)

Example: Different Plots (cont’d)

plot(mySampleSizeSummary, type = 2)

Example: Different Plots (cont’d)

plot(mySampleSizeSummary, type = 3)

Adaptive Analysis 📈

Calculate adjusted point estimates and confidence intervals
Some highlights:
- Automatic boundary recalculations during the trial for analysis with alpha spending approach, including under- and over-running
- Adaptive analysis tools for multi-arm trials
- Adaptive analysis tools for enrichment designs

Easy to understand R commands:

getStageResults()
getRepeatedConfidenceIntervals()
getAnalysisResults()

Simulation Tool 🧪

Obtain operating characteristics of different designs:

Assessment of adaptive sample size recalculation strategies
Assessment of treatment selection strategies in multi-arm trials
Assessment of population selection strategies in enrichment designs

Easy to understand R commands:

getSimulation[MultiArm / Enrichment][Means / Rates / Survival / Counts]()

Example:

getSimulationMeans()
getSimulationMultiArmMeans()
getSimulationEnrichmentMeans()

Package Validation Concept ☑️

Why is rpact a reliable R package?

Formal validation inspired by “GAMP 5” principles
- Validation documentation with > 8,000 pages
As few dependencies as possible:
- Imports: Rcpp
- Suggests: testthat, ggplot2, R6
High test coverage:
- rpact 4.3.0 (Wassmer and Pahlke 2025): 39,128 unit tests (82% test coverage)
testPackage(): installation qualification on a client computer or the Roche servers (because of SLA in place)
We assume that base/core R is validated/reliable (see R-FDA.pdf)

Professional Support: SLA 🤝

Service Level Agreement (SLA) with RPACT
Be part of the “RPACT User Group”
\(\rightarrow\) yearly customer meetings
Get technical software support for written support requests
Get company specific training
Get company specific software validation documentation for each rpact release on CRAN

Get access to the members area at connect.rpact.com
Make an rpact installation qualification on Roche company servers / containers with your personal testPackage() token and secret
Determine the direction of rpact future development activities
Optional support for health authority interactions
Optional consultation to support specific clinical trials

RPACT Cloud

RPACT Cloud ☁️

Graphical user interface
Web based usage without local installation on nearly any device ☁️
Provides an easy entry to rpact
Starting point for your R Markdown or Quarto reports
Helpful to learn/demonstrate the usage of rpact in a user friendly and intuitive way
Online available at rpact.cloud [free demo version]
Over the past year, four major pharmaceutical companies have licensed RPACT Cloud for internal installation

Start Page

Design

Reporting

Design Comparison

crmPack

Overview 📦

Specialized R package for dose escalation trials
Initial CRAN release in 2016, publication by Sabanés Bové et al. (2019)
Higher flexibility compared to other software, thanks to its modular design
Easy extensibility and adoption of new designs
Produces visual and numeric outputs for easy interpretation
Facilitates simulations to assess the performance under various scenarios

Large group of contributors 🏢

Clara Beck (Bayer)
Oliver Boix (Bayer)
Prerana Chandratre (Bayer)
Robert Adams (Bayer)
Dimitris Kontos (ClinBAY/Bayer)
Jiawen Zhu (Genentech)
Ziwei Liao (Genentech)
Daniel Sabanés Bové (Roche/RCONIS)
John Kirkpatrick (Roche/Astellas)

Giuseppe Palermo (Roche)
Guanya Peng (Roche)
Doug Kelkhoff (Roche)
Wojciech Wojciak (Roche)
Marlene Schulte-Goebel (Merck)
Burak Kuersad Guenhan (Merck)
Wai Yin Yeung (University of Lancaster/Roche)
Thomas Jaki (University of Lancaster/Cambridge/Regensburg)

Framework

crmPack provides a highly flexible framework for the design and analysis of dose escalation trials. This schematic illustrates the framework’s key components and their interactions:

crmPack Framework

Example: Dose Escalation Design 💊

library(crmPack)

empty_data <- Data(doseGrid = c(1, 3, 5, 10, 15, 20, 25, 40, 50, 80, 100))

# Initialize the CRM model.
my_model <- LogisticLogNormal(
    mean = c(-0.85, 1),
    cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
    ref_dose = 56
)

# Choose the rule for selecting the next dose.
my_next_best <- NextBestNCRM(
    target = c(0.2, 0.35),
    overdose = c(0.35, 1),
    max_overdose_prob = 0.25
)

# Choose the rule for the cohort-size.
my_size1 <- CohortSizeRange(
    intervals = c(0, 30),
    cohort_size = c(1, 3)
)

Example: Dose Escalation Design 💊 (cont’d)

my_size2 <- CohortSizeDLT(
    intervals = c(0, 1),
    cohort_size = c(1, 3)
)
my_size <- maxSize(my_size1, my_size2)

# Choose the rule for stopping.
my_stopping1 <- StoppingMinCohorts(nCohorts = 3)
my_stopping2 <- StoppingTargetProb(
    target = c(0.2, 0.35),
    prob = 0.5
)
my_stopping3 <- StoppingMinPatients(nPatients = 20)
my_stopping <- (my_stopping1 & my_stopping2) | my_stopping3

# Choose the rule for dose increments.
my_increments <- IncrementsRelative(
    intervals = c(0, 20),
    increments = c(1, 0.33)
)

Example: Dose Escalation Design 💊 (cont’d)

# Initialize the design.
design <- Design(
    model = my_model,
    nextBest = my_next_best,
    stopping = my_stopping,
    increments = my_increments,
    cohort_size = my_size,
    data = empty_data,
    startingDose = 3
)

# Run simulations.
truth <- function(dose) plogis(-0.85 + 1 * log(dose / 56))
sims <- simulate(
  design,
  nsim = 10,
  seed = 123,
  truth = truth,
  McmcOptions = McmcOptions(rng_kind = "Marsenne-Twister", rng_seed = 123)
)

Different Plots

plot(sims)

Different Plots (cont’d)

summary(sims, truth = truth) |> plot()

Professional Support: SLA 🤝

Service Level Agreement (SLA) with RPACT
Get technical software support for written support requests
Get company specific training
Get company specific software validation documentation for each crmPack release on CRAN (coming in 2026)

Make a crmPack installation qualification on each Roche computer (coming in 2026)
Determine the direction of crmPack future development activities (many new features in recent release!)
Optional support for health authority interactions
Optional consultation to support specific clinical trials

Executive summary 📝

We presented two open source R packages for clinical trial design:
- rpact for confirmatory clinical trial designs
- crmPack for dose escalation trial designs
Both packages are supported by RPACT
- Service Level Agreement (SLA) available for companies, universities, etc.
RPACT Cloud is a user-friendly platform designed to simplify and enhance the process of clinical trial simulations for researchers and practitioners
- Free demo version available at rpact.cloud

References

Emerson, Scott. 2000. S+SeqTrial Technical Overview. Insightful Corporation. https://www.msi.co.jp/splus/addon/pdf/seqtech.pdf.

Sabanés Bové, Daniel, Wai Yin Yeung, Giuseppe Palermo, and Thomas Jaki. 2019. “Model-Based Dose Escalation Designs in R with crmPack.” Journal of Statistical Software 89 (10): 1–22. https://doi.org/10.18637/jss.v089.i10.

Wassmer, Gernot, and Werner Brannath. 2025. Group Sequential and Confirmatory Adaptive Designs in Clinical Trials. Springer. https://link.springer.com/book/10.1007/978-3-031-89669-9.

Wassmer, Gernot, and Friedrich Pahlke. 2025. rpact: Confirmatory Adaptive Clinical Trial Design and Analysis. https://CRAN.R-project.org/package=rpact.

Designing Clinical Trials in R with rpact and crmPack

Disclaimer

This talk: two examples of open-source software revolution!

Related: The Rousseeuw Prize for R!

Clinical Trial Ecosystem in R 🌏

rpact

Overview 📦

Developed by RPACT 🏢

Trial Designs 🔬

Sample Size and Power Calculation 💻

Example: Sample Size Calculation 🧮

Example: Sample Size Calculation Output

Example: Different Plots

Example: Different Plots (cont’d)

Example: Different Plots (cont’d)

Adaptive Analysis 📈

Simulation Tool 🧪

Package Validation Concept ☑️

Professional Support: SLA 🤝

RPACT Cloud

RPACT Cloud ☁️

Start Page

Design

Reporting

Design Comparison

crmPack

Overview 📦

Large group of contributors 🏢

Framework

Example: Dose Escalation Design 💊

Example: Dose Escalation Design 💊 (cont’d)

Example: Dose Escalation Design 💊 (cont’d)

Different Plots

Different Plots (cont’d)

Professional Support: SLA 🤝

Executive summary 📝

References