Responsible De-identification of Clinical Trial Data
Best practices around clinical trial data sharing
The number of clinical trial data sets that are available for secondary analysis has been growing steadily over the last year. While this is very encouraging, there is a need to also ensure that this data is properly de-identified.
This webinar, Responsible De-identification of Clinical Trial Data, will set expectations about what is needed in a de-identification solution, and provide attendees a pragmatic look at emerging standards in this area.
In this presentation, we will first hear about the data expectations from Project Data Sphere®, an independent initiative of the non-profit CEO Roundtable on Cancer. Then, we will describe the risk-based de-identification methodology that was recently published in the IOM report “Sharing Clinical trial Data – Maximizing benefit, Minimizing Risk” and illustrate how it can be applied in practice. This will be followed by an introduction to the PhUSE de-identification working group for CDISC data models, their goals and deliverables.
Kald Abdullah, Project Data Sphere
Khaled El Emam, Privacy Analytics
Jean-Marc Ferran, PhUSE