Personalized medicine is the tailoring of treatment to the individual characteristics of patients. Once a treatment has been tested in a clinical trial and its effect overall quantified, it would be of great value to be able to use the baseline patients’ characteristics to identify patients with larger/lower benefits from treatment, for a more personalized approach to therapy.
I will show a previously published statistical method, aimed at identifying patients’ profiles associated
to larger treatment benefits applied to randomized clinical trials in multiple sclerosis, demonstrating the possibility to refine and personalize the treatment effect estimated in randomized studies by using the baseline demographic and clinical characteristics of the included patients. The
method can be applied to any randomized trial in any medical condition to create a treatment-specific score associated to different levels of response to the treatment tested in the trial. This is an easy and affordable method toward therapy personalization, indicating patient profiles related to a larger benefit from a specific drug, which may have implications for taking clinical decisions in everyday clinical practice.