Quantifying the benefit-risk trade-off for individual patients in a clinical trial: principles and antithrombotic case study

Background: A treatment 's overall favorable bene fit -risk pro file does not imply that every individual patient will bene fit from the treatment. Objectives: To describe a statistical methodology for quantifying the bene fit -risk tradeoff in individual patients. Methods: The method requires a large randomized controlled trial containing a primary ef ficacy outcome and a primary safety outcome, for instance, the Thrombin Receptor Antagonist in Secondary Prevention of Atherothrombotic Ischemic Events - Thrombolysis in Myocardial Infarction 50 placebo -controlled trial of vorapaxar in 17 779 patients following myocardial infarction. Multivariate regression models predict each individual patient 's risk of ischemic events (bene fit) and major bleeding events (harm) based on their pro file. Hence, each patient 's predicted bene fit from vorapaxar (reduction in ischemic events) and predicted risk (increase in bleeding events) were estimated. The relative importance of ischemic and bleeding events based on links to all -cause mortality was quanti fied, although the limitations of such weightings are noted. Results: Overall results demonstrated both clear bene fit and harm from vorapaxar. Substantial interindividual variation in both bene fit and risk facilitated distinguishing patients with a favorable bene fit -risk trade-off from those who did not. Such findings were applied to recommend vorapaxar in as many as 98.3% of patients in which a favorable mortality -weighted bene fit -risk trade-off was present, in 77.2% of patients with ischemic bene fit 20% greater than bleeding risk, or in as few as 45.5% of patients if an annual decrease in ischemic risk of >= 0.5% was also required. Conclusion: While overall randomized controlled trials of treatment bene fit vs risk are valuable, models determining each individual patient 's estimated absolute bene fit and risk provide more useful insight regarding patient -speci fic bene fit-risk trade-offs to better enable personalized therapeutic decision-making.