Turning Early-Stage Data Into Clear Development Decisions
Early-stage studies often require important development decisions before enough data are available to provide definitive evidence of treatment benefit.
In this case study, a biotech company developing a therapy targeting fibrosis and inflammation needed to evaluate safety and efficacy over a 12-week study in patients with suspected NASH and liver fibrosis.
Because the study was small and short, liver biopsy data alone were unlikely to provide sufficient evidence for a clear development decision. MMS developed a quantitative decision-making framework using KerusCloud® to define meaningful Go, Consider, and Stop outcomes and evaluate the probability of reaching each decision under different treatment-effect scenarios.
Key Benefits
- How quantitative decision criteria can strengthen early-stage clinical development decisions.
- How KerusCloud® simulation can help establish appropriate sample sizes and decision thresholds.
- How biomarker variability and treatment-effect assumptions can be incorporated into a decision framework.
- How simulation can evaluate the probability of reaching Go, Consider, or Stop decisions.
- How stress-testing for missing data can improve the robustness of clinical trial decision criteria.
- How evidence-based decision frameworks can help de-risk and accelerate development programs.
casestudy
Creating Go/No Go Decision-Making Criteria for Early Stage Studies
Discover how MMS used quantitative simulation to establish robust Go, Consider, and Stop criteria, helping a biotech sponsor make confident development decisions from limited early-stage clinical data.
