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Session 6: Tool Sets and Methodologies for Complex Innovative Designs
Session Chair(s)
Pritibha Singh, MBA, MSC
Principal Investigator for Doctoral Research, ETH Zurich, Switzerland
May F Mo, MBA, MS
Associate Vice President, Biostatistics, Amgen, United States
The evaluation, optimization and comparisons of complex clinical trial designs often rely on modeling and simulation. Extensive simulations are often required and critical for the assessment of important operating characteristics such as probabilities of erroneous conclusions and bias in estimates, and for better understanding of trade-offs between trial designs such as average sample size, cost, and trial duration. Good practices in planning and executing clinical trial simulations and comprehensively reporting simulation results in terms of example trials and operating characteristics will be discussed.
Learning Objective : At the conclusion of this session, participants should be able to:
- Apply machine learning into the drug development processes
- Calculate the Probability of Success using Bayesian methodology
- Recognize opportunities to use data more effectively
Speaker(s)
AI and ML in Drug Development and Trial Design
Senior Vice President, Global Biometrics and Data Sciences, Bristol Myers Squibb, United States
Simulation in Adaptive and Platform Trial Design
Director of Research. Senior Statistical Scientist, Berry Consultants, United States
Multi-Objective Approach to Assist in Selection of Efficient Clinical Trial Operation Design with Respect to Minimizing Expected Recruitment Time and Geographic Site Footprint
Executive Director, Data Science, Amgen, United States
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