Use of Real-World Data for in-silico Trials and Innovative Designs: Recent Successes and Current Perspectives
Real-world data (RWD) are transforming clinical research. By supplementing randomized controlled trials (RCTs), RWD can de-risk studies and improve generalizability. Innovative designs, from hybrid RCTs to registry-based trials, are gaining traction, with regulators setting clear standards for their use.
This webinar will share case studies, practical methodologies, and key considerations to help you apply RWD effectively while balancing risks and opportunities.
Learning Outcomes:
- When are innovative trial designs using RWD most appropriate?
- How can technical and regulatory feasibility be assessed?
- What RWD sources are most suitable?
- Which statistical methods offer the best fit in these studies?
Meet the Experts:
Billy Amzal
Head of Strategic Consulting
Over the past 25 years, Billy has developed statistical methodologies to inform and support strategic decision making in healthcare. Prior to joining Phastar, Billy led the model-based drug development team at Novartis. Then, he developed and led implementation of statistical methodologies for high impact research sponsored by public and global Health Agencies (EFSA, Global Fund, US NIH).
Billy has been Senior VP at Certara, leading data and decision analytics, CEO and Chief Statistician at Quinten Health, an AI company pioneering digital twins and disease modeling, and continues to serve as a statistical expert for public health organizations such as the EMA, WHO, and Gates Foundation.
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Wei Liu
Associate Director of Biostatistics
Wei is an Associate Director of Biostatistics with over 20 years of experience in both academia and industry. She holds PhDs in Epidemiology and Genetics, as well as an MS in Statistics, specializing in statistical design, data analysis strategies, and outcome evaluation for both clinical trials and observational studies.
An author of more than 60 peer-reviewed publications in leading medical journals, Wei has served as lead statistician in conducting longitudinal data analysis, survival models, and causal inference modeling across diverse fields such as oncology, neuropsychology, and sickle cell disease.
