MedianaDesigner-package: Efficient Simulation-Based Power and Sample Size Calculations...

In MedianaDesigner: Power and Sample Size Calculations for Clinical Trials

Efficient Simulation-Based Power and Sample Size Calculations for a Broad Class of Late-Stage Clinical Trials

Description

The MedianaDesigner package provides 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:

• 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 (experimental module).

• Traditional trials with multiple objectives (experimental module).

• Traditional trials with cluster-randomized designs (experimental module).

For more information on this package, visit https://mediana.us/free-software/. The technical manuals with a detailed description of the statistical methodology implemented in each module are available at https://medianasoft.github.io/MedianaDesigner. Additional information and multiple case studies can be found in the online manual at https://medianasoft.github.io/MedianaDesigner.

Details

Package:	MedianaDesigner
Type:	Package
Version:	0.13
Date:	2023-08-27
License:	GPL-3

Key functions included in the package:

• ADSSMod: Simulation-based design of adaptive trials with data-driven sample size or event count re-estimation.

• ADSSModApp: Graphical user interface for designing adaptive trials with data-driven sample size or event count re-estimation.

• ADTreatSel: Simulation-based design of adaptive trials with data-driven treatment selection.

• ADTreatSelApp: Graphical user interface for designing adaptive trials with data-driven treatment selection.

• ADPopSel: Simulation-based design of adaptive trials with data-driven population selection.

• ADPopSelApp: Graphical user interface for designing adaptive trials with data-driven population selection.

• FutRule: Simulation-based selection of an optimal futility stopping rule at an interim analysis.

• FutRuleApp: Graphical user interface for an optimal selection of a futility stopping rule.

• EventPred: Blinded simulation-based event prediction in trials with an event-driven design.

• EventPredPriorDistribution: Calculation of the parameters of prior gamma distributions.

• EventPredApp: Graphical user interface for event prediction.

• ADRand: Simulation-based design of adaptive trials with response-adaptive randomization.

• ADRandApp: Graphical user interface for designing adaptive trials with response-adaptive randomization.

• MultAdj: Simulation-based design of traditional trials with multiple objectives.

• MultAdjApp: Graphical user interface for power calculations in traditional trials with multiple objectives.

• ClustRand: Simulation-based design of cluster-randomized trials.

• ClustRandApp: Graphical user interface for power calculations in cluster-randomized trials.

• GenerateReport: Simulation report for any module.

The package comes with one example data set:

• EventPredData: Example data set to illustrate event prediction in event-driven trials.

Multiple case studies are included in this vignette to illustrate the use of this package for designing clinical trials with data-driven design elements:

• ADSSModExample1: Simulation-based design of an adaptive trial with sample size re-estimation (normally distributed endpoint).

• ADSSModExample2: Simulation-based design of an adaptive trial with sample size re-estimation (binary endpoint).

• ADSSModExample3: Simulation-based design of an adaptive trial with event count re-estimation (time-to-event endpoint).

• ADTreatSelExample1: Simulation-based design of an adaptive trial with treatment selection (normally distributed endpoint).

• ADTreatSelExample2: Simulation-based design of an adaptive trials with treatment selection (binary endpoint).

• ADTreatSelExample3: Simulation-based design of an adaptive trials with treatment selection (time-to-event endpoint).

• ADPopSelExample1: Simulation-based design of an adaptive trial with population selection (normally distributed endpoint).

• ADPopSelExample2: Simulation-based design of an adaptive trials with population selection (binary endpoint).

• ADPopSelExample3: Simulation-based design of an adaptive trials with population selection (time-to-event endpoint).

• FutRuleExample1: Simulation-based selection of an optimal futility stopping rule (normally distributed endpoint).

• FutRuleExample2: Simulation-based selection of an optimal futility stopping rule (binary endpoint).

• FutRuleExample3: Simulation-based selection of an optimal futility stopping rule (time-to-event endpoint).

• EventPredExample: Simulation-based event prediction in trials with an event-driven design (time-to-event endpoint).

• ADRandExample: Simulation-based design of an adaptive dose-finding trial with response-adaptive randomization (normally distributed endpoint).

• MultAdjExample1: Simulation-based power calculations in Phase III trials with multiple dose-placebo comparisons.

• MultAdjExample2: Simulation-based power calculations in Phase III trials with multiple endpoints.

• MultAdjExample3: Simulation-based power calculations in Phase III trials with multiple endpoints and multiple dose-placebo comparisons.

• ClustRandExample1: Simulation-based design of a cluster-randomized trial (normally distributed endpoint).

• ClustRandExample2: Simulation-based design of a cluster-randomized trial (binary endpoint).

Author(s)

Mediana (www.mediana.us). We would like to thank multiple individuals, including Thomas Brechenmacher (IQVIA), Douglas McNair (Gates Foundation) and Thomas Peppard (Certara), for their feedback that helped us improve the package and add new features.

References

Ahn, C., Heo, M., Zhang, S. (2015). Sample Size Calculations for Clustered and Longitudinal Outcomes in Clinical Research. Chapman and Hall/CRC.

Alosh, M., Bretz, F., Huque, M. (2014). Advanced multiplicity adjustment methods in clinical trials. Statistics in Medicine. 33, 693-713.

Bagiella, E., Heitjan, D.F. (2001). Predicting analysis times in randomized clinical trials. Statistics in Medicine. 20, 2055-2063.

Chuang-Stein, C., Kirby, S., French, J., Kowalski, K., Marshall, S., Smith, M. K. (2011). A quantitative approach for making go/no-go decisions in drug development. Drug Information Journal. 45, 187-202.

Dmitrienko, A., Bretz, F., Westfall, P.H., et al. (2009). Multiple testing methodology. Multiple testing problems in pharmaceutical statistics. Dmitrienko, A., Tamhane, A.C., Bretz, F. (editors). New York: Chapman and Hall/CRC Press.

Dmitrienko, A., Tamhane, A.C. (2011). Mixtures of multiple testing procedures for gatekeeping applications in clinical trials. Statistics in Medicine. 30, 1473-1488.

Dmitrienko, A., D'Agostino, R. Sr. (2013). Traditional multiplicity adjustment methods in clinical trials. Statistics in Medicine. 32, 5172-5218.

Dmitrienko, A., Kordzakhia, G., Brechenmacher, T. (2016). Mixture-based gatekeeping procedures for multiplicity problems with multiple sequences of hypotheses. Journal of Biopharmaceutical Statistics. 26, 758-780.

Dmitrienko, A., Paux, G. (2017). Subgroup analysis in clinical trials. Clinical Trial Optimization Using R. Dmitrienko, A., Pulkstenis, E. (editors). Chapman and Hall/CRC Press, New York.

Dmitrienko, A., D'Agostino, R.B. (2018). Multiplicity considerations in clinical trials. New England Journal of Medicine. 378, 2115-2122.

Hayes, R.J., Moulton, L.H. (2009). emphCluster Randomised Trials: A Practical Approach. Chapman and Hall/CRC.

Herson, J. (1979). Predictive probability early termination plans for Phase II clinical trials. Biometrics. 35, 775-783.

Kordzakhia, G., Brechenmacher, T., Ishida, E., Dmitrienko, A., Zheng, W.W., Lie, D.F. (2018). An enhanced mixture method for constructing gatekeeping procedures in clinical trials. Journal of Biopharmaceutical Statistics. 28, 113-128.

Millen, B., Dmitrienko, A., Ruberg, S., Shen, L. (2012). A statistical framework for decision making in confirmatory multi-population tailoring clinical trials. Drug Information Journal. 46, 647-656.

Wang, D., Cui, L., Zhang, L., Yang, B. (2014). An ROC approach to evaluate interim go/no-go decision-making quality with application to futility stopping in the clinical trial designs. New Developments in Statistical Modeling, Inference and Application. Jin, Z., Liu, M., Luo, X. (editors). Springer, New York. 121-147.

Wassmer, G., Brannath, W. (2016). Group Sequential and Confirmatory Adaptive Designs in Clinical Trials. New York: Springer.

MedianaDesigner documentation built on Aug. 28, 2023, 9:06 a.m.