A treatment’s overall favourable benefit-risk profile does not imply that every individual patient will benefit from the treatment. We describe a statistical methodology for quantifying the benefit-risk trade-off in individual patients.
The method requires a large RCT containing a primary efficacy outcome and a primary safety outcome: for instance the TIMI-50 placebo-controlled trial of vorapaxar in 17,779 patients following a 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, though the limitations of such weightings are noted.
Overall results demonstrated both clear benefit and harm from vorapaxar. Substantial inter-individual variation in both benefit and risk, facilitates distinguishing patients with a favourable benefit-risk trade-off from those who did not. Such findings are applied to recommend vorapaxar in as many as 98.3% of patients with a favourable mortality-weighted benefit-risk trade-off was present, in 77.2% of patients with ischaemic 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% is also required.
While overall RCT of treatment benefit versus risk are valuable, models determining each individual patient’s estimated absolute benefit and risk provides more useful insight regarding patient-specific benefit-risk trade-off, to better enable personalized therapeutic decision-making.

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