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bayesCT - Tool for Simulation and Analysis of Adaptive Bayesian Clinical Trials

Authors: Thevaa Chandereng, Donald Musgrove, Tarek Haddad, Graeme Hickey, Timothy Hanson and Theodore Lystig

Overview

bayesCT is a R package for simulation and analysis of adaptive Bayesian randomized controlled trials under a range of trial designs and outcome types. Currently, it supports Gaussian, binomial, and time-to-event. The bayesCT package website is available here. Please note this package is still under development.

Installation

Prior to analyzing your data, the R package needs to be installed. The easiest way to install bayesCT is through CRAN:

install.packages("bayesCT")

There are other additional ways to download bayesCT. The first option is most useful if want to download a specific version of bayesCT (which can be found at https://github.com/thevaachandereng/bayesCT/releases):

devtools::install_github("thevaachandereng/bayesCT@vx.xx.x")

# or 

devtools::install_version("bayesCT", version = "x.x.x", repos = "http://cran.us.r-project.org")

The second option is to download through GitHub:

devtools::install_github("thevaachandereng/bayesCT")

After successful installation, the package must be loaded into the working space:

library(bayesCT)

Usage

See the vignettes for usage instructions and example.

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

License

bayesCT is available under the open source GNU General Public License, version 3.

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Version

Install

install.packages('bayesCT')

Monthly Downloads

66

Version

0.99.3

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Thevaa Chandereng

Last Published

July 1st, 2020

Functions in bayesCT (0.99.3)

survivalBACT

Time-to-event outcome for Bayesian Adaptive Trials
binomialdata

Binomial dataset for analyzing adaptive Bayesian trials
analysis

Analysis wrapper function
beta_prior

Beta prior for for control and treatment group
historical_binomial

Historical data for binomial distribution
binomialBACT

Binomial counts for Bayesian Adaptive Trials
historical_normal

Historical data for normal distribution
binomial_analysis

Analyzing Bayesian trial for binomial counts
impute

Imputation wrapper
randomization

Randomization allocation
data_normal

Data file for normal analysis
historical_survival

Historical data for survival analysis
randomize

Randomization scheme wrapper
normalBACT

Normal distribution for Bayesian Adaptive Trials
hypothesis

Hypothesis wrapper
enrollment_rate

Enrollment rate wrapper
pw_exp_impute

Imputes time-to-event outcomes.
gamma_prior

Gamma prior for for control and treatment group
pw_exp_sim

Simulates time-to-event outcomes.
%>%

Pipe operator
normaldata

Gaussian dataset for analyzing adaptive Bayesian trials
enrollment

Simulating enrollment dates
data_survival

Data file for survival analysis
simulate

Simulation wrapper for binomial and normal.
study_details

Details of the clinical study
survival_outcome

Piecewise constant hazard rates and the cutpoint for control and treatment group
survivaldata

Time-to-event dataset for analyzing adaptive Bayesian trials
data_binomial

Data file for binomial analysis
normal_analysis

Analyzing Bayesian trial for normal mean data
normal_outcome

Parameters for treatment and control in normal case
binomial_outcome

Proportion of an event in control and treatment
survival_analysis

Analyzing Bayesian trial for time-to-event data