The Replicability of Analysis Results Using Synthetic Clinical Trial Data

Synthetic Data is increasingly used to share and enhance sensitive health data. However, regulators, biopharma companies, and funders of health research question whether this technology can result in replicable results while maintaining patient privacy for health data research.

Join our webinar to discover insights from a groundbreaking Nature Scientific Reports publication, a collaboration between Aetion scientists and the Electronic Health Information Laboratory (EHIL) at the Children's Hospital of Eastern Ontario (CHEO). Delve into the methodology and the key findings demonstrating synthetic data's efficacy in producing similar analytic conclusions to the real data while protecting patient privacy.

Key Topics:

• Definition of replicability in health data research
• Optimal strategies for synthetic data generation
• Best practices methodology for drawing replicable conclusions when analyzing synthetic data (RWD or clinical trial data)
• Synthetic data as a privacy-enhancing technology (PET)
• Impact of this methodology in action by sharing results of the analysis of 8 oncology trials

Your Hosts

• Lucy Mosquera Sr. Director, Generate Operations and Data Science, Aetion. Research affiliate, Electronic Health Information Laboratory (EHIL)
• Khaled El Emam Professor, University of Ottawa
• Samer El Kababji Postdoctoral Fellow, Electronic Health Information Laboratory (EHIL)