

# Mediana 

[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/Mediana)](https://cran.r-project.org/package=Mediana)
[![CRAN\_Logs\_Badge](http://cranlogs.r-pkg.org/badges/Mediana)](https://cran.r-project.org/package=Mediana)
[![CRAN\_Logs\_Badge\_Total](http://cranlogs.r-pkg.org/badges/grand-total/Mediana)](https://cran.r-project.org/package=Mediana)

`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.

# Installation

Get the released version from CRAN:

``` r
install.packages("Mediana")
```

Or the development version from github:

``` r
# install.packages("devtools")
devtools::install_github("gpaux/Mediana", build_opts = NULL)
```

## Vignettes

`Mediana` includes 3 vignettes. In particular, an introduction of the
package and several case studies:

``` r
vignette(topic = "mediana", package = "Mediana")
vignette(topic = "case-studies", package = "Mediana")
```

# Online Manual

A detailed online manual is accessible at
<http://gpaux.github.io/Mediana/>

# References

## Clinical trial optimization using R book

[Clinical Trial Optimization Using
R](https://www.crcpress.com/Clinical-Trial-Optimization-using-R/Dmitrienko/p/book/9781498735070)
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.

## Publications

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:

  - Dmitrienko, A., Paux, G., Brechenmacher, T. (2016). \[Power
    calculations in clinical trials with complex clinical objectives.\]
    Journal of the Japanese Society of Computational Statistics. 28,
    15-50.\](<https://www.jstage.jst.go.jp/article/jjscs/28/1/28_1411001_213/_article>)
  - Dmitrienko, A., Paux, G., Pulkstenis, E., Zhang, J. (2016).
    \[Tradeoff-based optimization criteria in clinical trials with
    multiple objectives and adaptive designs.\] Journal of
    Biopharmaceutical Statistics. 26,
    120-140.\](<http://www.tandfonline.com/doi/abs/10.1080/10543406.2015.1092032?journalCode=lbps20>)
  - Paux, G. and Dmitrienko A. (2018). \[Penalty-based approaches to
    evaluating multiplicity adjustments in clinical trials: Traditional
    multiplicity problems.\] Journal of Biopharmaceutical Statistics.
    28, 146-168.(<https://doi.org/10.1080/10543406.2017.1397010>)
  - Paux, G. and Dmitrienko A. (2018). \[Penalty-based approaches to
    evaluating multiplicity adjustments in clinical trials: Advanced
    multiplicity problems.\] Journal of Biopharmaceutical Statistics.
    28, 169-188.(<https://doi.org/10.1080/10543406.2017.1397011>)

# Citation

If you find `Mediana` useful, please cite it in your publications:

``` r
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")'.
```
