Methods for deriving risk difference (absolute risk reduction) from a meta-analysis

Decision making requires the trade-off of benefits and harms, which in turn requires knowledge of the absolute treatment effect on binary patient outcomes. Decision makers make judgments about how this absolute effect relates to their decisional thresholds.1 For example, a systematic review showed that compared with carotid artery stenting, carotid endarterectomy was associated with a significant reduction in the risk of stroke in patients with symptoms of average risk (risk ratio 0.77, 95% confidence interval 0.63 to 0.94). Endarterectomy, however, increased the risk of myocardial infarction (risk ratio 2.15, 95% confidence interval 1.27 to 3.61).2 Trading off these two outcomes in a clinical setting, a guideline, or decision modeling cannot be done solely based on these relative effects. Instead, we need to know the exact treatment effect in absolute terms (ie, how many strokes were prevented and how many additional myocardial infarctions were caused in 1000 patients who received endarterectomy…
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