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Bayesian Methods in Adaptive Clinical Trial Designs
- 1. Bayesian Methods inAdaptive Clinical Trial Designs Scientific Basis, Real-Time Data Use, and Applications in Oncology & Rare Diseases • Presenter: Sumanthmayur M R • Affiliation: Lovely Professional University • Key Themes: Bayesian statistics, adaptive trials, oncology, rare diseases
- 2. Introduction to AdaptiveClinical Trials • Dynamic Trial Design: Allows pre-planned modifications during trial execution based on accumulating data • Enhanced Efficiency: Improves resource use, shortens timelines, and maintains ethical standards • Bayesian Role: Enables real-time updates and informed decisions through probabilistic modeling • Special Use Cases: Highly suitable for oncology and rare diseases with limited patient populations
- 3. Core Concepts ofBayesian Statistics • Bayesian Philosophy: Merges prior knowledge with new evidence to form updated beliefs • Probabilistic Modeling: Represents uncertainty through probability distributions • Posterior Calculation: Posterior = Prior × Likelihood from data • Real-Time Adaptation: Dynamically updates trial variables like treatment allocation and sample size
- 4. Scientific Basis forAdaptive Randomization • Responsive Allocation: Adjusts patient assignment ratios using interim analysis results • Bayesian Framework: Leverages posterior probabilities to favor superior treatments • Statistical Efficiency: Boosts power while reducing required sample sizes • Model Examples: Employs Bayesian hierarchical models and Thompson sampling
- 5. Real-Time Data Updates •Data-Driven Decisions: Facilitates responsive trial adjustments during execution • Bayesian Recalibration: Continuously updates model parameters as new evidence emerges • Efficiency Gains: Enables early stopping and dynamic sample size adaptation • Digital Integration: Utilizes platforms for real- time data capture and analytics
- 6. Bayesian Dose-Finding Designs Early-PhaseFocus Primarily used in Phase I/II oncology trials to assess dosing safety and efficacy Model-Based Methods CRM and BOIN models guide dose escalation or de- escalation Bayesian Logic Posterior probabilities inform decisions on dose tolerability Patient Safety Minimizes exposure to toxic or ineffective doses
- 7. Case Study –Oncology: I-SPY 2 Trial Platform Trial Design Utilizes a Bayesian adaptive model for breast cancer therapies Biomarker Integration Assigns treatments based on molecular profiles and interim outcomes Learning System Continuously updates regimen efficacy using real- time data Accelerated Development Speeds up discovery and approval by prioritizing promising candidates
- 8. Case Study –Rare Diseases: MLD Trial • Low Patient Numbers: Adaptive design addresses scarcity of subjects in rare disease trials • External Data Use: Bayesian models integrate historical and real-world evidence • Flexible Endpoints: Trial adjusted recruitment and outcomes in real-time • Regulatory Success: Enabled early approval and improved patient access
- 9. Challenges and Considerations ModelComplexity Requires expert statistical knowledge for accurate implementation Regulatory Barriers Demand for transparency and pre-specified protocols Computational Load Real-time analysis imposes high software and hardware demands Ethical Sensitivity Dynamic allocation raises concerns around patient fairness
- 10. Conclusion and FutureOutlook • Methodological Shift: Bayesian designs are reshaping clinical trial standards • Enhanced Capabilities: Greater flexibility, ethics, and statistical power • Regulatory Embrace: Gaining acceptance from global agencies like FDA and EMA • Emerging Directions: Integration with AI, real- world data, and decentralized frameworks