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Existing trials had not taken enough consideration of their populationrepresentativeness, which can lower the effectiveness when the treatment isapplied in real-world clinical practice. We analyzed the eligibility criteriaof Bevacizumab colorectal cancer treatment trials, assessed their a priorigeneralizability, and examined how it affects patient outcomes when applied inreal-world clinical settings. To do so, we extracted patient-level data from alarge collection of electronic health records (EHRs) from the OneFloridaconsortium. We built a zero-inflated negative binomial model using a compositepatient-trial generalizability (cPTG) score to predict patients clinicaloutcomes (i.e., number of serious adverse events, (SAEs)). Our study resultsprovide a body of evidence that 1) the cPTG scores can predict patientoutcomes; and 2) patients who are more similar to the study population in thetrials that were used to develop the treatment will have a significantly lowerpossibility to experience serious adverse events.

Assessing the Validity of a a priori Patient-Trial Generalizability  Score using Real-world Data from a Large Clinical Data Research Network: A  Colorectal Cancer Clinical Trial Case Study