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DesignCTPB  

Design Clinical Trials with Potential Biomarker Effect
View on CRAN: Click here


Download and install DesignCTPB package within the R console
Install from CRAN:
install.packages("DesignCTPB")

Install from Github:
library("remotes")
install_github("cran/DesignCTPB")

Install by package version:
library("remotes")
install_version("DesignCTPB", "1.1.3")



Attach the package and use:
library("DesignCTPB")
Maintained by
Yitao Lu
[Scholar Profile | Author Map]
First Published: 2021-01-21
Latest Update: 2021-09-21
Description:
Applying 'CUDA' 'GPUs' via 'Numba' for optimal clinical design. It allows the user to utilize a 'reticulate' 'Python' environment and run intensive Monte Carlo simulation to get the optimal cutoff for the clinical design with potential biomarker effect, which can guide the realistic clinical trials.
How to cite:
Yitao Lu (2021). DesignCTPB: Design Clinical Trials with Potential Biomarker Effect. R package version 1.1.3, https://cran.r-project.org/web/packages/DesignCTPB. Accessed 21 Feb. 2025.
Previous versions and publish date:
0.4.0 (2021-01-21 10:10), 1.1.1 (2021-04-05 15:50)
Other packages that cited DesignCTPB R package
View DesignCTPB citation profile
Other R packages that DesignCTPB depends, imports, suggests or enhances
Complete documentation for DesignCTPB
Functions, R codes and Examples using the DesignCTPB R package
Some associated functions: alpha.split . designCTPB . phat . proportion . res_strong . res_weak . 
Some associated R codes: constrains.R . designCTPB.R . sampling.R . smoothing.R .  Full DesignCTPB package functions and examples
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