BayesCTDesign

0.6.1

Two Arm Bayesian Clinical Trial Design with and Without Historical Control Data

0packages depend

4.3Kdownloads / year

–test coverage

13/13checks pass

Overview

About

Maintained by Barry EgglestonFirst published 2018-08-143 releasesCRAN page ↗GitHub ↗

A set of functions to help clinical trial researchers calculate power and sample size for two-arm Bayesian randomized clinical trials that do or do not incorporate historical control data. At some point during the design process, a clinical trial researcher who is designing a basic two-arm Bayesian randomized clinical trial needs to make decisions about power and sample size within the context of hypothesized treatment effects. Through simulation, the simple_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about treatment effect,control group characteristics, and outcome. If the clinical trial researcher has access to historical control data, then the researcher can design a two-arm Bayesian randomized clinical trial that incorporates the historical data. In such a case, the researcher needs to work through the potential consequences of historical and randomized control differences on trial characteristics, in addition to working through issues regarding power in the context of sample size, treatment effect size, and outcome. If a researcher designs a clinical trial that will incorporate historical control data, the researcher needs the randomized controls to be from the same population as the historical controls. What if this is not the case when the designed trial is implemented? During the design phase, the researcher needs to investigate the negative effects of possible historic/randomized control differences on power, type one error, and other trial characteristics. Using this information, the researcher should design the trial to mitigate these negative effects. Through simulation, the historic_sim() function will estimate power and other user specified clinical trial characteristics at user specified sample sizes given user defined scenarios about historical and randomized control differences as well as treatment effects and outcomes. The results from historic_sim() and simple_sim() can be printed with print_table() and graphed with plot_table() methods. Outcomes considered are Gaussian, Poisson, Bernoulli, Lognormal, Weibull, and Piecewise Exponential. The methods are described in Eggleston et al. (2021) doi:10.18637/jss.v100.i21.

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("BayesCTDesign", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("begglest/BayesCTDesign")

Development version from begglest/BayesCTDesign. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-bayesctdesign

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-BayesCTDesign

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-BayesCTDesign

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed).

Health

CRAN checks

13OK

Slowest check: 2.3 min · r-devel-linux-x86_64-fedora-clang

Code health

None

Tests · ratio 0.00

not tracked

Coverage

100%

Documentation · exports

Dependencies · direct

Check history

OK2026-03-10
14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

READMENoVignettesNopkgdown siteNoNEWSYes · 33% structuredCode of conductNoContributing guideNo

Examples that run: 100%
Documented parameters: 100%
Return-value docs: 100%
References docs: 0%

Downloads

4.3K

CRAN downloads in the past year

Rank #9,695 · ~12/day · ~360/mo

Releases R versions

Daily download trend is not available in this view yet.

33930 days

1.1K90 days

4.3K1 year

Compare downloads with other packages →

Also on153 r2u48 autocran30 c2d4u

Repository

GitHub ↗ begglest/bayesctdesign

2Stars

1Forks

0Open issues

0Open PRs

0Releases

6Commits

1Contributors

6 commits · Last activity 2021-11-28

Stars over time

2023-11-19 · 22026-07-07 · 2

Repository practices

Upstream repositoryBeta

3 development-tooling and community-health practices detected across 3 families in the upstream repository

Checks run against github.com/begglest/bayesctdesign on 2026-07-19.

CRAN release process (1)

cran-comments.md

Docs source (1)

README.Rmd

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v1 (2026-07-18)

Dependencies

Declared dependencies

3 external dependencies (excludes base and recommended)

Depends (1)

R >= 3.5.0

Imports (5)

eha ggplot2survivalreshape2stats

LinkingTo (0)

none

Suggests (0)

none

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

Code & Tests

People & History

People (5)

Maintainer (1)

Barry Eggleston

Maintainer, Author

Authors (5)

Maintainer, Author

Funder, rth, Author

Author

Author

Author

Funders (1)

Diane Catellier

Funder, rth, Author

Package Timeline

3 releases. Pick two to compare their code metrics. R releases are shown for context.

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
R
R 4.4.0 released · 2024-04-24
R
R 4.3.0 released · 2023-04-21
R
R 4.2.0 released · 2022-04-22
0.6.1Latest
2021-11-30 · current release · diff ↗
R
R 4.1.0 released · 2021-05-18
R
R 4.0.0 released · 2020-04-24
0.6.0
2019-08-02 · diff ↗
R
R 3.6.0 released · 2019-04-26
0.5.0
2018-08-14
R
R 3.5.0 released · 2018-04-23

Package metadata

First published: 2018-08-14
Total releases: 3 / 8 yrs
License: GPL-3 OSI
Minimum R: ≥ 3.5.0
Download size: 73 KB
Installed size: not tracked yet
With dependencies: not tracked yet

Appears in task views

Bayesian Inference Clinical Trial Design, Monitoring, Analysis and Reporting

Report a problem with this page →