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

ABSTRACT

BACKGROUND: A treatment's overall favorable benefit-risk profile does not imply that every individual patient will benefit from the treatment. OBJECTIVES: To describe a statistical methodology for quantifying the benefit-risk trade-off in individual patients. METHODS: The method requires a large randomized controlled trial containing a primary efficacy 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 (benefit) and major bleeding events (harm) based on their profile. Hence, each patient's predicted benefit 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 quantified, although the limitations of such weightings are noted. RESULTS: Overall results demonstrated both clear benefit and harm from vorapaxar. Substantial interindividual variation in both benefit and risk facilitated distinguishing patients with a favorable benefit-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 benefit-risk trade-off was present, in 77.2% of patients with ischemic benefit 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 benefit vs risk are valuable, models determining each individual patient's estimated absolute benefit and risk provide more useful insight regarding patient-specific benefit-risk trade-offs to better enable personalized therapeutic decision-making.