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Abstract Details

Activity Number: 37
Type: Contributed
Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #306887
Title: A New Approach for Evaluating Benefit-Risk in Anticoagulation Studies: a Case Study
Author(s): Vijay Chauhan and May Tang*+
Companies: Alpha Stats Inc. and Alpha Stats Inc.
Address: 2109 Cedar Village Blvd, East Brunswick, NJ, 08816, United States
Keywords: Net clinical benefit ; Net clinical outcome ; Composite endpoint ; Stroke ; Atrial fibrillation ; Benefit-risk

The benefit-risk has been quantified in some studies as the net clinical benefit (NCB) or net clinical outcome of a composite endpoint consisting of efficacy and safety endpoints. The efficacy and safety endpoints included in a composite endpoint are implicitly assumed to be clinically equally important. However, there is a need to consider weight for each endpoint, according to relative clinical importance when estimating NCB. The aim of this study was to evaluate benefit-risk as NCB, weighing efficacy and safety endpoints. We estimated NCB as the aggregate odds ratio by combining the risk ratios for efficacy and safety endpoints, alternatively using weight of 1.00 to each endpoint, using the inverse of variance of risk ratio as weight, and using weight based on clinical importance. Published data from dabigatran (RE-LY) and apixaban (ARISTOTLE) trials were used to illustrate our method. Estimates of NCB from various combinations of endpoints were robust, leading to an inference that the NCB was higher from apixaban than dabigatran. The analyses using the inverse of variance of risk ratio as the weight were considered more efficient, because of narrow confidence intervals. The aggregate odds ratio is a simple and robust means of combining different outcomes. This method can be equally applied to any set of multiple outcomes in any therapeutic areas. Since the trials are mainly powered for the primary efficacy endpoint, we suggest weighing the risk ratios of different endpoints by the inverse of variance of the risk ratio for estimating NCB.

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