knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
The drugdevelopR package enables you to plan phase II/III drug development programs with optimal sample size allocation and go/no-go decision rules. The assumed true treatment effects can be fixed or modelled by a prior distribution. The corresponding R Shiny application has a graphic user interface and thus makes it accessible for users without prior knowledge of R. Fast computing is made possible by parallel programming. Theoretical foundations for this package were laid in the dissertation "Integrated Planning of Pilot and Subsequent Confirmatory Study in Clinical Research – Finding Optimal Designs in a Utility-Based Framework" by Stella Erdmann at the Institute of Medical Biometry at the University of Heidelberg.
On the package webpage, we supply full documentation of all functions as well as a tutorial for getting started with drugdevelopR.
Install the development version of the package directly from GitHub using the following code:
if(!require(devtools)) { install.packages("devtools"); require(devtools)} devtools::install_github("Sterniii3/drugdevelopR") ```` and access the drugdevelopR App via [https://web.imbi.uni-heidelberg.de/drugdevelopR/](https://web.imbi.uni-heidelberg.de/drugdevelopR/). ## Usage Here is a basic example for applying drugdevelopR to a drug development program with a normally distributed outcome: ```r library(drugdevelopR) # Optimize optimal_normal(Delta1 = 0.625, Delta2 = 0.8, fixed = FALSE, # treatment effect n2min = 20, n2max = 400, # sample size region stepn2 = 4, # sample size step size kappamin = 0.02, kappamax = 0.2, # threshold region stepkappa = 0.02, # threshold step size c2 = 0.675, c3 = 0.72, # maximal total trial costs c02 = 15, c03 = 20, # maximal per-patient costs b1 = 3000, b2 = 8000, b3 = 10000, # gains for patients alpha = 0.025, # one-sided significance level beta = 0.1, # 1 - power w = 0.6, in1 = 300, in2 = 600, # weight and amount of information a = 0.25, b = 0.75) # truncation values
The drugdevelopR package provides the functions
to plan optimal phase II/III drug development programs with
endpoints, where the treatment effect is modelled on a prior distribution. Optimal phase II/III drug development planning with fixed treatment effects can be done with the help of the R Shiny application basic.
Extensions to the basic setting are:
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