A Case Study of De-identifying a Clinical Trial Data Set
Interested in learning more about de-identifying a clinical trial data set? Don’t miss this webinar!
A Case Study of De-identifying a Clinical Trial Data Set Using a Risk-based Method
Many trial sponsors are starting to implement or have implemented clinical trial transparency initiatives for individual patient data (IPD). However, releasing unmodified IPD would put the clinical trial participants’ privacy at risk. New guidelines for the de-identification of clinical trials have been published, but there have been no demonstrations of the de-identification methods on real clinical trial data.
Privacy Analytics, in collaboration with Sanofi Pasteur, de-identified a vaccine clinical trial following these new guidelines. Featuring key speakers, Khaled El Emam, CEO of Privacy Analytics, and Stephen Korte, Research Analyst, this webinar will describe the process for de-identifying a Sanofi-Pasteur vaccine clinical trial dataset and what the outcome was, and clarify how to apply re-identification risk analysis to clinical trial data sets.
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About our speakers
Dr. Khaled El Emam is the founder and CEO of Privacy Analytics. He is also a senior investigator at the Children’s Hospital of Eastern Ontario Research Institute, a professor at the University of Ottawa and held the Canada Research Chair in Electronic Health Information from 2005 to 2015. He is a renowned expert in statistical de-identification and re-identification risk measurement. He is one of only a handful of individual experts in North America qualified to anonymize Protected Health Information under the HIPAA Privacy Rule.
Stephen Korte is a research analyst who anonymizes data sets. He prototyped and developed risk measurement algorithms, publicly presents demonstrations of Privacy Analytics’ software and methodology course, and developed an exam to certify individuals as experts in the field of anonymization. He holds a M.Sc in Physics and has a background in computational science and mathematical modeling.