Existing trials had not taken enough consideration of their population representativeness, which can lower the effectiveness when the treatment is applied in real-world clinical practice. We analyzed the eligibility criteria of Bevacizumab colorectal cancer treatment trials, assessed their a priori generalizability, and examined how it affects patient outcomes when applied in real-world clinical settings. To do so, we extracted patient-level data from a large collection of electronic health records (EHRs) from the OneFlorida consortium. We built a zero-inflated negative binomial model using a composite patient-trial generalizability (cPTG) score to predict patients’ clinical outcomes (i.e., number of serious adverse events, [SAEs]). Our study results provide a body of evidence that 1) the cPTG scores can predict patient outcomes; and 2) patients who are more similar to the study population in the trials that were used to develop the treatment will have a significantly lower possibility to experience serious adverse events.

Learning Objective: Understand the concept of the clinical trial generalizability and how eligibility criteria affect the a prior generalizability.
Learn how to leverage electronic health record (EHR) data to extract patients' clinical outcomes such as adverse events.
Learn trial generalizability relates to the clinical outcomes when the treatment is applied in real-world clinical practice.


Qian Li (Presenter)
University of Florida

Zhe He, Florida state university
Yi Guo, University of Florida
Hansi Zhang, University of Florida
Thomas George, University of Florida
William Hogan, University of Florida
Neil Charness, Florida state university
Jiang Bian, University of Florida

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