Making Clinical Trial Decisions With Greater Confidence
Recruitment challenges can put pressure on clinical trial timelines, sample size, and the ability to make timely development decisions. When new external evidence also suggests that the expected treatment effect may differ from the original assumptions, sponsors need a rigorous way to evaluate whether the trial design should adapt.
In this case study, MMS used KerusCloud® to evaluate adaptive clinical trial designs and quantify how different interim analyses, stopping rules, and treatment-effect assumptions could influence study outcomes.
The analysis provided a quantitative framework for selecting an adaptive design that could improve study efficiency while maintaining appropriate statistical control.
Key Benefits
- How simulation can help address clinical trial recruitment challenges
- How to evaluate adaptive clinical trial designs under changing assumptions
- How interim analyses can support earlier efficacy and futility decisions
- How different stopping rules affect the probability of study success
- How to balance sample size efficiency with the risk of incorrect decisions
- How KerusCloud® can support quantitative clinical trial design optimization
casestudy
Driving Decision Making With an Adaptive Clinical Trial Design
Discover how MMS used simulation-driven adaptive trial design to address recruitment challenges, evaluate interim decision-making, and optimize the balance between study efficiency and statistical risk.
