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.
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")'.
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
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.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
).
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
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
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.
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.
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).
Revision of the generation of dropout time.
Correction of the NormalParamAdj
function.
Correction of the FisherTest
function.