Online manual
This package supports efficient simulation-based power and sample size calculations for a broad class of late-stage clinical trials, including Phase II trials, seamless Phase II/III trials and Phase III trials.
The following modules are currently included in the package:
- Module A: Adaptive trials with data-driven sample size or event count re-estimation.
- Module B: Adaptive trials with data-driven treatment selection.
- Module C: Adaptive trials with data-driven population selection.
- Module D: Optimal selection of a futility stopping rule.
- Module E: Blinded event prediction in event-driven trials.
- Module F: Adaptive trials with response-adaptive randomization (experimental module).
- Module G: Traditional trials with multiple objectives (experimental module).
- Module H: Traditional trials with cluster-randomized designs (experimental module).
Development version
As noted on the main page, the latest stable version of the package can be downloaded from CRAN and the latest development version is available on Github. This version is easy to install directly from GitHub using the devtools package. After installing this package, submit the following code to install the latest version of the MedianaDesigner package on your computer:
devtools::install_github("medianasoft/MedianaDesigner")
Feedback
Please let us know if you have questions about the R package or underlying methodology. You can contact us at info at mediana.us. In addition, you could submit questions, issues or feature requests on the issues page.
Technical manuals
The technical manuals with a detailed description of the statistical methodology implemented in each module can also be found on Mediana’s web site:
- Adaptive trials with data-driven sample size or event count re-estimation.
- Adaptive trials with data-driven treatment selection.
- Adaptive trials with data-driven population selection.
- Optimal selection of a futility stopping rule.
- Blinded event prediction in event-driven trials.
- Adaptive trials with response-adaptive randomization.
- Traditional trials with multiple objectives.
- Traditional trials with with cluster-randomized designs.
Case studies
Multiple case studies have been created to help users come up to speed with the package. This online manual is updated on a regular basis and more case studies will be added in the near future.
Module A: Adaptive trials with data-driven sample size/event count re-estimation
The following case studies illustrate the process of designing Phase III trials with data-driven sample size/event count re-estimation using the ADSSMod function:
Module B: Adaptive trials with data-driven treatment selection
The following case study illustrates the process of designing Phase III trials with data-driven treatment selection using the ADTreatSel function:
Module C: Adaptive trials with data-driven population selection
The following case study illustrates the process of designing Phase III trials with data-driven population selection using the ADPopSel function:
Module D: Optimal selection of a futility stopping rule
The following case study demonstrates how to set up optimal futility stopping rules using the FutRule function:
Module E: Blinded event prediction in event-driven trials
The following case study provides an illustration for the EventPred function:
Module F: Adaptive trials with response-adaptive randomization
The following case study illustrates the process of designing a dose-finding Phase II trial with response-adaptive randomization using the ADRand function:
Module G: Traditional trials with multiple objectives
The following case studies illustrate power calculations in Phase III trials with multiple dose-control comparisons and multiple endpoints using the MultAdj function:
Module H: Traditional trials with cluster-randomized designs
The following case study illustrates the process of designing studies with cluster-randomized designs using the ClustRand function: