Author: Valentina Shakhnovich, MD on June 04, 2020 
As a patient, I care about clinical trial results only if they are applicable to me and the particulars of my condition, including my age, sex, race, disease duration, symptom severity, comorbid conditions, etc. For example, reading about treatment outcomes in a clinical trial conducted in 55- to 80-year-old men and women was not at all helpful to me, or my doctor, for a diagnosis that I received at the age of 36. I use this personal anecdote to emphasize that in order to provide value, clinical trial results must be generalizable to the patient population at large. Fortunately, as investigators in the Big Data Era, with appropriate informatics tools, we can achieve this goal.
As every investigator knows, study design is a balance of controlling for potential confounders to isolate study intervention effect vs. allowance of inter- and intra-personal variability in study subjects to accurately represent the patient population outside of the study setting, where the intervention will ultimately be used. A quick, simplistic approach to assessing clinical trial generalizability is to compare the patient characteristics of the study subjects, and/or the study inclusion-exclusion criteria, to the target patient population in the real world. A more rigorous approach is to compare treatment outcomes in clinical practice (through observational cohort studies, for example) to outcomes reported in the original trial; however, this type of posteriori generalizability assessment is only possible after trial completion, and additional phase 3/4 trials may need to be conducted to make study results more generalizable. With less than half of the published clinical trials addressing generalizability a priori, and less than one-third finding positive generalizability, there is significant room for improvement, as highlighted by the authors of “Clinical Trial Generalizability Assessment in the Big Data Era: A Review,” published in Clinical and Translational Science. This comprehensive review discusses how today’s widespread use of the electronic medical record offers a unique, real-time opportunity to improve clinical trial generalizability assessment a priori, giving investigators a golden opportunity to leverage information-rich patient databases to adjust study design before a clinical trial begins ̶ thus ensuring capture of all relevant real-world patient characteristics to be included in the trial population and avoiding critical information gaps for actual real-world patients.
Image by He et al. Clin. Trans. Sci. https://ascpt.onlinelibrary.wiley.com/doi/10.1111/cts.12764. Published 2020. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics.
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