Real-World Data Set Parameters and Synthesization for Matching Identity in Clinical Protocols

A main challenge for clinical protocol evaluations is the lack of public real-world data sets due to the private nature of patient information. We studied the case of phase 1 clinical trials where the identity of participants is key in determining their eligibility to participate in a trial. Our objective is to use the experience from our study to present a list of parameters to help generate data sets that closely match their real-world counterparts. We also examine existing tools and address their limitations with a tool of our own. Through the development of our clinical trial protocol, we discovered a field selection that proved to be efficient to detect a participant's identity, which may be used by other researchers in their protocols.