Mediana R package
New FAQ page
Clinical trial optimization using R
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 version 1.0.6 of the Mediana R package has been released on 13 February 2018. This latest stable version can be downloaded from the CRAN website. The principal revisions compared to the previous version include the following 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
AnalysisStackfunction (see Analysis stack).
Addition of the
ExtractAnalysisStackfunction to extract a specific set of results in an
AnalysisStackobject (see Analysis stack).
Creation of a vignette to describe the functions implementing the adjusted p-values (
AdjustPvalues) and one-sided simultaneous confidence intervals (
Minor revisions of the generated report. Note that the dependency to the
ReporteRsR package has been removed to facilitate the package installation in case of issue with java. However, in order to generate a Word-based report, the ReporteRs R package must be installed.
It is now possible to use an option to specify the desirable direction of the treatment effect in a test, e.g.,
larger = TRUEmeans that numerically larger values are expected in the second sample compared to the first sample and
larger = FALSEotherwise. 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).
Some bug fixes.
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.
Expert and development teams
Package design: Alex Dmitrienko (Mediana Inc.).
Extended development team: Thomas Brechenmacher (Novartis), Fei Chen (Johnson and Johnson), Ilya Lipkovich (Quintiles), Ming-Dauh Wang (Lilly), Jay Zhang (MedImmune), Haiyan Zheng (Osaka University).
Expert team: Keaven Anderson (Merck), Frank Harrell (Vanderbilt University), Mani Lakshminarayanan (Pfizer), Brian Millen (Lilly), Jose Pinheiro (Johnson and Johnson), Thomas Schmelter (Bayer).
Install the latest version of the Mediana package from CRAN using the install.packages command in R:
Alternatively, you can download the package from the CRAN website.
The up-to-date development version can be found and installed directly from the GitHub web site. You need to install the devtools package and then call the install_github function in R:
Potential installation’s issue
When installing Mediana package, an error could occur if a java version >= 1.6 is not installed. Java is used in the ReporteRs R package which is required in the Mediana R package to generate Word report.
system("java -version") should return java version ‘1.6.0’ or greater.
In order to ensure a proper installation, it is highly recommended to install the latest version of Java in the same architecture of R (32-bit or 64-bit).
The latest version can be found at https://www.java.com/en/download/manual.jsp.
Clinical Scenario Evaluation Framework
The Mediana R package was developed to provide a general software implementation of the Clinical Scenario Evaluation (CSE) framework. This framework introduced by Benda et al. (2010) and Friede et al. (2010) recognizes that sample size calculation and power evaluation in clinical trials are high-dimensional statistical problems. This approach helps decompose this complex problem by identifying key elements of the evaluation process. These components are termed models:
- Data models define the process of generating trial data (e.g., sample sizes, outcome distributions and parameters).
- Analysis models define the statistical methods applied to the trial data (e.g., statistical tests, multiplicity adjustments).
- Evaluation models specify the measures for evaluating the performance of the analysis strategies (e.g., traditional success criteria such as marginal power or composite criteria such as disjunctive power).
Find out more about the role of each model and how to specify the three models to perform Clinical Scenario Evaluation by reviewing the dedicated pages (click on the links above).
Multiple case studies are provided on this web site to facilitate the implementation of Clinical Scenario Evaluation in different clinical trial settings using the Mediana package. These case studies will be updated on a regular basis.
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.
- 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.
- 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.
- 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.