CRAN/E | BayesCTDesign

BayesCTDesign

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

Installation

About

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.

Citation BayesCTDesign citation info
github.com/begglest/BayesCTDesign
Bug report File report

Key Metrics

Version 0.6.1
R ≥ 3.5.0
Published 2021-11-30 849 days ago
Needs compilation? no
License GPL-3
CRAN checks BayesCTDesign results

Downloads

Yesterday 7 0%
Last 7 days 59 -24%
Last 30 days 287 -2%
Last 90 days 1.035 +21%
Last 365 days 3.774 -14%

Maintainer

Maintainer

Barry Eggleston

beggleston@rti.org

Authors

Barry Eggleston

cre / aut

Doug Wilson

aut

Becky McNeil

aut

Joseph Ibrahim

aut

Diane Catellier

fnd

rth

aut

Material

NEWS
Reference manual
Package source

In Views

Bayesian

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

BayesCTDesign archive

Depends

R ≥ 3.5.0

Imports

eha ≥ 2.9.0
ggplot2 ≥ 2.2.1
survival ≥ 2.41-3
reshape2 ≥ 1.4.3
stats ≥ 3.5.0