Category: Clinical trial simulations.
The Clinical Scenario Evaluation (CSE) framework has been introduced to facilitate the process of performing clinical trial simulations in late-stage trials with complex designs and analysis strategies. This approach helps decompose this multidimensional problem into a small number of components that define the core building blocks of the overall CSE framework. These components are termed models and are defined as follows:
- Data models define the process of generating trial data, e.g., sample sizes, primary endpoint and its parameters, trial design’s 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.
A structured framework based on the three components enables clinical trial sponsors to carry out a systematic quantitative assessment of the operating characteristics of candidate designs and analysis methods and characterize their performance in multiple settings. Specifically, within the data and analysis models, several sets of statistical assumptions or analysis strategies can be specified and the combination of these two models represents a clinical scenario. CSE focuses on the application of the criteria defined in the evaluation model to the clinical scenarios of interest.
For more information on the Clinical Scenario Evaluation framework and its applications to streamlining clinical trial simulations, see
- Benda, N., Branson, M., Maurer, W., Friede, T. (2010). Aspects of modernizing drug development using clinical scenario planning and evaluation. Drug Information Journal. 44, 299-315.
- Friede, T., Nicholas, R., Stallard, N., Todd, S., Parsons, N. R., Valdes-Marquez, E., Chataway, J. (2010). Refinement of the clinical scenario evaluation framework for assessment of competing development strategies with an application to multiple sclerosis. Drug Information Journal. 44, 713-718.
- Dmitrienko, A., Pulkstenis, E. (editors). (2017). Clinical Trial Optimization Using R. Chapman and Hall/CRC Press, New York.
The Mediana package provides an R-based implementation of the Clinical Scenario Evaluation approach to enable general simulation-based power/sample size calculations in clinical trials. This approach also plays a central role in Mediana Designer to support simulation-based calculations in fixed-sample trials.