Introduction: Efficacy of a new treatment may be accompanied by safety concerns. Methods to assess who has a favourable risk-benefit trade-off are lacking.
Methods: We describe methods to predict the individual patient’s absolute benefit and risk based on multivariable models using baseline characteristic. We develop an algorithm for clinical use for individualising a patient's treatment strategy, which also considers the relative importance of each events, and capture uncertainty surrounding the risk-benefit trade-off.
Results: We illustrate this approach in major cardiovascular studies, including in the TIMI 50 trial of vorapaxar vs. placebo in post-myocardial infarction, where ischaemic benefits are accompanied by increased risk of major bleeding and a meta-analysis of 6 studies in coronary patients receiving a stent, with the goal of identifying the relative risks of thrombosis and bleeding for individual high risk patients and
Conclusions: Our findings illustrate how quantitative methods can help to individualize treatment to patients who will most benefit.
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