The use of cloud computing for trial simulation, alongside advances in custom-developed software for this purpose, allow biostatisticians to generate many more design options and to simulate those against a multitude of expected treatment effects to create trials that are more robust against several likely execution scenarios.
Here, we explain the concept of design at a large scale and highlight the reasons that make this approach a necessity in modern drug development.
Design due diligence
Simulation at scale means no stone left unturned. With increased pressure to bring drugs to market quicker, and the ever-changing landscape of treatment options available to patients, there is often heightened uncertainty about treatment effect assumptions in the clinical trial design stage.
Leveraging cloud computing power, study design teams can evaluate all possible treatment and control effects, either as a range of discrete values or as probability distributions and evaluate proposed study designs against such execution scenarios. Cloud resources are essential for rapid simulation of many treatment-effect variations and eliminate the need for sponsors to maintain their own computing grids. These variations in treatment effects can then be ranked by likelihood for a Bayesian analysis informed by priors or be observed discreetly to identify an acceptable window for study success. Other uncertainties in such areas as recruitment or dropout rates can be similarly examined.
Thorough sensitivity analysis
Large-scale simulation means more effective sensitivity analyses of all relevant trial execution parameters. Once a design archetype has been agreed upon, cloud computing can be leveraged to minutely vary trial design parameters such as the expected sample size, number of events, information fraction for interim analysis, and efficacy and futility boundaries. Varying these parameters allows for a much more thorough fine-tuning of variables to uncover efficiencies in trial cost, duration, and probability of success. In a macro environment of reduction in overall R&D spend, thorough sensitivity analysis ensures resources are applied responsibly.
High-efficiency collaboration
Simulation at scale means high-efficiency collaboration across the drug development team. With all the uncertainties outlined above, there is an increased need for real-time response to internal governance feedback, and external regulatory concerns. Since all study design parameters and execution scenarios have been meticulously identified and simulated, drug development teams can examine results side-by-side and discuss trade-offs between design selections and respond to any concerns or interest from governance bodies. Cloud-driven simulation work at the outset of this process ensures all options are on the table, and cloud-native design selection tools and algorithms can be employed for confident decision-making. If additional variables or uncertainties are uncovered during the design selection process, they can be incorporated using the same computing resources for rapid results.
Key takeaways
Cloud computing can be leveraged to simulate at a much larger scale than possible in the past. Designing in the context of a wide range of potential treatment effects allows more confident design selections and, eventually, more effective and successful trials.
To learn more about Cytel’s cloud-powered advanced simulation and analytics platform for clinical trial design, please contact us.
Interested in learning more? Valeria Mazzanti, Associate Director of Customer Success, and J. Kyle Wathen, Vice President, Scientific Strategy and Innovation, discuss the integration of East® and R. With this new capability, users have greater latitude in selecting input parameters, such as analysis types and test statistics, beyond those that are native to the software. Click to register:
About Boaz N. Adler
Boaz Adler is Director of Global Product Engagement at Cytel. He has served as a Solutions Consultant and Analyst for Life Sciences companies and Health-Tech organizations for over a decade. Boaz’s interests are focused on tech and novel services innovations that contribute to more coherent and robust evidence generation across the drug development cycle. At Cytel, Boaz enhances the connection between Cytel’s software development team and its clients and supports clients in clinical trial optimization projects using Cytel’s cutting-edge technology.
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