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Mediana is an R package which provides a general
framework for clinical trial simulations based on the Clinical Scenario
Evaluation approach. The package supports a broad class of data models
(including clinical trials with continuous, binary, survival-type and
count-type endpoints as well as multivariate outcomes that are based on
combinations of different endpoints), analysis strategies and commonly
used evaluation criteria.
Find out more at http://gpaux.github.io/Mediana/ and check out the case studies.
Get the released version from CRAN:
install.packages("Mediana")Or the development version from github:
# install.packages("devtools")
devtools::install_github("gpaux/Mediana", build_opts = NULL)Mediana includes 3 vignettes. In particular, an
introduction of the package and several case studies:
vignette(topic = "mediana", package = "Mediana")
vignette(topic = "case-studies", package = "Mediana")A detailed online manual is accessible at http://gpaux.github.io/Mediana/
Clinical Trial Optimization Using R explores a unified and broadly applicable framework for optimizing decision making and strategy selection in clinical development, through a series of examples and case studies.It provides the clinical researcher with a powerful evaluation paradigm, as well as supportive R tools, to evaluate and select among simultaneous competing designs or analysis options. It is applicable broadly to statisticians and other quantitative clinical trialists, who have an interest in optimizing clinical trials, clinical trial programs, or associated analytics and decision making.
This book presents in depth the Clinical Scenario Evaluation (CSE) framework, and discusses optimization strategies, including the quantitative assessment of tradeoffs. A variety of common development challenges are evaluated as case studies, and used to show how this framework both simplifies and optimizes strategy selection. Specific settings include optimizing adaptive designs, multiplicity and subgroup analysis strategies, and overall development decision-making criteria around Go/No-Go. After this book, the reader will be equipped to extend the CSE framework to their particular development challenges as well.
Mediana R package has been widely used to implement the
case studies presented in this book. The detailed description and R code
of these case studies are available on this website.
The Mediana package has been successfully used in
multiple clinical trials to perform power calculations as well as
optimally select trial designs and analysis strategies (clinical trial
optimization). For more information on applications of the
Mediana package, download the following papers:
If you find Mediana useful, please cite it in your
publications:
citation("Mediana")
#>
#> To cite package 'Mediana' in publications use:
#>
#> Gautier Paux and Alex Dmitrienko. (2019). Mediana: Clinical Trial Simulations. R
#> package version 1.0.8. http://gpaux.github.io/Mediana/
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {Mediana: Clinical Trial Simulations},
#> author = {Gautier Paux and Alex Dmitrienko.},
#> year = {2019},
#> note = {R package version 1.0.8},
#> url = {http://gpaux.github.io/Mediana/},
#> }
#>
#> ATTENTION: This citation information has been auto-generated from the package DESCRIPTION
#> file and may need manual editing, see 'help("citation")'.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.