Abstract 18743: Bayesian Comparative Analysis for Prevention of Stroke and Net Clinical Benefit of Novel Oral Anticoagulants in Non-Valvular Atrial Fibrillation

Abstract

Background: The recent emergence of novel oral anticoagulants dabigatran, rivaroxaban and apixaban for prevention of stroke and systemic embolization in atrial fibrillation are attractive alternatives to warfarin. Despite this, the differences in efficacy and bleeding events between these medications remains unclear. To compare differential benefit, we performed a Bayesian analysis comparing the efficacy and safety parameters of the 3 novel agents. Methods: Using data from the 3 major trials investigating the novel oral anticoagulants, we conducted a Bayesian analysis using null prior probabilities, which is particularly suited to indirect comparisons. The primary endpoints of stroke and systemic embolization were assessed, along with safety endpoints of major bleeding. A Bayesian analysis was performed on each group, defining probabilities of benefit for the posterior probability. From this data, net clinical benefit calculations were performed and posterior probabilities were obtained. Results: Posterior probabilities for stroke and systemic embolization indicate the apixaban, rivaroxaban and dabigatran have an 87.3%, 88.7%, and 66.3% probability of benefit over warfarin, respectively. Major bleeding events were found to have a 98.6%, 88.0% and 55.0% probability of benefit over warfarin. Net clinical benefit analysis indicates benefit with apixaban and rivaroxaban that was greater than dabigatran. Conclusions: Using Bayesian analytic techniques, all of the novel oral anticoagulants were found to be non-inferior to warfarin. However, the effectiveness in prevention of stroke suggests that both apixaban and rivaroxaban significantly outperform dabigatran. Net clinic benefit and safety endpoints support the use of apixaban as opposed to rivaroxaban and dabigatran. While more data emerges investigating the differential benefit of these new medications, Bayesian techniques offer novel comparative statistics which can guide clinical decision making.