Efficient power and sample size calculations for late-phase clinical trials.
Clinical trial simulations play a central role in the design of modern clinical trials. Simulation-based approaches enable clinical project teams to efficiently evaluate key characteristics of complex trial designs and can be extended to support clinical trial optimization, i.e., examine multiple options to arrive at the best-performing trial design and data analysis strategies.
Clinical trial simulation methods are often applied to facilitate the design of adaptive clinical trials. Adaptive approaches to trial designs have found numerous applications in early-phase and late-phase clinical trials. A key feature of an adaptive approach is its ability to react to emerging trends in the data over the course of the trial. Focusing on late-stage trials, the available options for data-driven adaptations include:
- Increasing the number of patients in the trial if the emerging data suggests that the original sample size may have been too small.
- Selecting the best treatment or most promising subset of the trial’s population based on a pre-defined patient characteristic.
Adaptive trial designs provide a number of advantages over traditional designs without data-driven elements. The former are generally more efficient from a statistical perspective and help improve the probability of success in the trial. Adaptive designs that support treatment or population selection result in better estimates of treatment effects compared to traditional designs. From an ethical perspective, adaptive designs help ensure that patients are not exposed to ineffective therapies or those with an undesirable risk-benefit profile.
Adaptive designs have gained widespread acceptance in confirmatory clinical trials and the general adaptive design framework has been endorsed in regulatory guidelines released by the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA).
MedianaDesigner
MedianaDesigner is a software package that provides efficient tools for designing late-stage clinical trials, including adaptive designs commonly used in Phase III and seamless Phase II/III trials. The package currently includes the following seven modules:
- Adaptive designs with data-driven sample size or event count re-estimation.
- Adaptive designs with data-driven treatment selection.
- Adaptive designs with data-driven population selection.
- Optimal selection of a futility stopping rule.
- Event prediction in event-driven trials.
- Adaptive designs with response-adaptive randomization.
- Traditional designs with multiple objectives.
This beta software tool has been designed to help statisticians and non-statisticians, who are new to clinical trial simulation and adaptive designs, come up to speed with this important class of trial designs. The software tool includes advanced software libraries to speed up clinical trial simulations, support for a graphical user interface and detailed Word-based simulation reports.
A free 10-part online training course on adaptive designs and clinical trial simulation that features the MedianaDesigner package is available here: https://dac-trials.org/resources/clinical-trial-simulation/medianadesigner-training/
Web Site: https://cloud.mediana.us/ | Login: Medianadesigner | Password: Rt5UKD8pzm
MEDIANA LLC PROVIDES STRATEGIC BIOSTATISTICAL CONSULTING SERVICES ACROSS KEY AREAS OF CLINICAL DRUG DEVELOPMENT, INCLUDING EARLY-STAGE AND LATE-STAGE INNOVATIVE TRIAL DESIGNS, SUBGROUP ANALYSIS AND BIOMARKER ASSESSMENT, AND BUILDS CUSTOM SOFTWARE TO IMPLEMENT ADAPTIVE DESIGNS AND ADVANCED STATISTICAL METHODS. FOR MORE INFORMATION, VISIT THE COMPANY’S WEB SITE AT MEDIANA.US.
Please note: Implementors of studies should also always refer to relevant regulatory standards and guidelines (e.g. FDA, EMA) to assist with study/protocol design.
