Clinical Trial Simulations

Provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation (CSE) 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.


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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.

Installation

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)

Vignettes

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")

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 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:

Citation

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")'.

News

Mediana 1.0.8

New features

  • Create an hexagon sticker for the package.

Bug fixes

  • Fix the calculation of intersection hypothesis pvalue when family weights is null for all gatekeeping procedures.

  • Revise the error fraction function to avoid floating point issue

  • Fix the images in the Case studies vignette

  • Revise the specification of serial and parallel parameters in MixtureGatekeepingAdj (matrix instead of list)

  • Revise the Outcome table generation function used for reporting

Mediana 1.0.7

Bug fixes

  • As the ReporteRs R package is not available on the CRAN anymore, the report generation feature has been revised using the officer and flextable R packages. These packages are now required to use the GenerateReport function.

Mediana 1.0.6

New features

  • Addition of the multinomial distribution (MultinomialDist, see Analysis model).

  • Addition of the ordinal logistic regression test (OrdinalLogisticRegTest, see Analysis model).

  • Addition of the Proportion statistic (PropStat, see Analysis model).

  • Addition of the Fallback procedure (FallbackAdj, see Analysis model).

  • Addition of a function to get the analysis results generated in the CSE using the AnalysisStack function (see Analysis stack).

  • Addition of the ExtractAnalysisStack function to extract a specific set of results in an AnalysisStack object (see Analysis stack).

  • Creation of a vignette to describe the functions implementing the adjusted p-values (AdjustPvalues) and one-sided simultaneous confidence intervals (AdjustCIs).

  • Minor revisions of the generated report

  • It is now possible to use an option to specify the desirable direction of the treatment effect in a test, e.g., larger = TRUE means that numerically larger values are expected in the second sample compared to the first sample and larger = FALSE otherwise. This is an optional argument for all two-sample statistical tests to be included in the Test object. By default, if this argument is not specified, it is expected that a numerically larger value is expected in the second sample (i.e., by default larger = TRUE).

Bug fixes

  • Due to difficulties for several users to install the Mediana R package because of java issue, the ReporteRs R package is not required anymore (remove from Imports). However, to be able to generate the report, the user will require to have the ReporteRs R package installed.

  • Minor revision to the two-sample non-inferiority test for proportions to ensure that the number of successes is not greater than the sample size

Mediana 1.0.5

New features

  • Addition of the AdjustPvalues function which can be used to get adjusted p-values from a Multiple Testing Procedure. This function cannot be used within the CSE framework but it is an add-on function to compute adjusted p-values.

  • Addition of the AdjustCIs function which can be used to get simultaneous confidence intervals from a Multiple Testing Procedure. This function cannot be used within the CSE framework but it is an add-on function to simultaneous confidence intervals.

  • Creation of vignettes

Bug fixes

  • Revision of the dropout generation mechanism for time-to-event endpoints.

Mediana 1.0.4

New features

  • Addition of the Fixed-sequence procedure (FixedSeqAdj, see Analysis model).

  • Addition of the Cox method to calculate the HR, effect size and ratio of effect size for time-to-event endpoint. This can be accomplished by setting the method argument in the parameter list to set-up the calculation based on the Cox method. (par = parameters(method = "Cox"), see Analysis model).

  • Addition of the package version information in the report.

Bug fixes

  • Revision of one-sided p-value computation for Log-Rank test.

  • Revision of the call for Statistic in the core function (not visible).

  • Revision of the function to calculate the Hazard Ratio Statistic (HazardRatioStat method). By default, this calculation is now based on the log-rank statistic ((O2/E2)/(O1/E1) where O and E are Observed and Expected event in sample 2 and sample 1. A parameter can be added using the method argument in the parameter list to set-up the calculation based on the Cox method (par = parameters(method = "Cox"), see Analysis model).

  • Revision of the function to calculate the effect size for time-to-event endpoint (EffectSizeEventStat method, based on the HazardRatioStat method)

  • Revision of the functions to calculate the ratio of effect size for continuous (RatioEffectSizeContStat method), binary (RatioEffectSizePropStat method) and event (RatioEffectSizeEventStat method) endpoint.

  • Revision of the function to generate the Test, Statistic, Design and result tables in the report.

Mediana 1.0.3

New features

  • Addition of the Beta distribution (BetaDist, see Data model).

  • Addition of the Truncated exponential distribution, which could be used as enrollment distribution (TruncatedExpoDist, see Data model).

  • Addition of the Non-inferiority test for proportion (PropTestNI, see Analysis model).

  • Addition of the mixture-based gatekeeping procedure (MixtureGatekeepingAdj see Analysis model).

  • Addition of a function to get the data generated in the CSE using the DataStack function (see Data stack).

  • Addition of a function to extract a specific set of data in a DataStack object (see Data stack).

  • Addition of the "Evaluation Model" section in the generated report describing the criteria and their parameters (see Simulation report).

Bug fixes

  • Revision of the generation of dropout time.

  • Correction of the NormalParamAdj function.

  • Correction of the FisherTest function.

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("Mediana")

1.0.8 by Gautier Paux, 5 months ago


http://gpaux.github.io/Mediana/


Report a bug at https://github.com/gpaux/Mediana/issues


Browse source code at https://github.com/cran/Mediana


Authors: Gautier Paux , Alex Dmitrienko.


Documentation:   PDF Manual  


Task views: Clinical Trial Design, Monitoring, and Analysis


GPL-2 license


Imports doParallel, doRNG, foreach, MASS, mvtnorm, stats, survival, utils

Suggests flextable, knitr, officer, rmarkdown, pander


See at CRAN