Bayesian Dose-Finding Designs using Pharmacokinetics (PK) for Phase I Clinical Trials

Statistical methods involving PK measures are provided, in the dose allocation process during a Phase I clinical trials. These methods enter pharmacokinetics (PK) in the dose finding designs in different ways, including covariates models, dependent variable or hierarchical models. This package provides functions to generate data from several scenarios and functions to run simulations which their objective is to determine the maximum tolerated dose (MTD).


CRAN Version

The dfpk R package provides an interface to fit Bayesian generalized (non-)linear mixed models using Stan, which is a C++ package for obtaining Bayesian inference using the No-U-turn sampler (see http://mc-stan.org/).

Description

dfpk package includes methods involving PK measures in the dose allocation process during a Phase I clinical trials. These methods enter PK in the dose finding designs in different ways, including covariates models, dependent variable or hierarchical models. This package provides functions to generate scenarios, and to run simulations which their objective is to determine the maximum tolerated dose (MTD).

Installation

Establish Version

A latest version of the package dfpk is available on CRAN and can be loaded via

install.packages("dfpk")
library(dfpk) 

Development Version

To install the dfpk package from GitHub, first make sure that you can install the rstan package and C++ toolchain by following these instructions. The program Rtools (available on https://cran.r-project.org/bin/windows/Rtools/) comes with a C++ compiler for Windows. On OS-X, you should install Xcode. Once rstan is successfully installed, you can install dfpk from GitHub using the devtools package by executing the following in R:

if (!require(devtools)){
  install.packages("devtools") 
  library(devtools) 
}
 
install_github("artemis-toumazi/dfpk")

If installation fails, please let us know by filing an issue.

Details on formula syntax, families and link functions, as well as prior distributions can be found on the help page of the dfpk function:

help(dfpk) 

FAQ

Can I avoid compiling models?

Unfortunately, fitting your model with dfpk, there is currently no way to avoid the compilation.

What is the best way to ask a question or propose a new feature?

You can either open an issue on github or write me an email to ([email protected]).

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Reference manual

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