  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.
