Cardiorenal outcome trials have transformed the management of cardiovascular and kidney disease, particularly through glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors. Treatment effects are commonly communicated using relative measures, including hazard ratios and relative risk reductions. Although essential for estimating treatment effects, these measures do not directly communicate the absolute magnitude of benefit experienced by patients. Absolute risk reduction (ARR) and number needed to treat (NNT) provide complementary information by incorporating underlying event risk and duration of follow-up. The distinction is clinically important. Similar relative treatment effects can produce substantially different absolute benefits depending on baseline risk, outcome definitions, follow-up duration, and study population characteristics. Conversely, a NNT from one trial should not be used to rank therapies across studies because differences in populations and endpoints strongly influence absolute event rates. For time-to-event outcomes, ARR should be estimated from cumulative event probabilities at a specified time point, preferably using appropriate survival methods. Hazard ratios reflect a lower estimated hazard over follow-up and should not be interpreted as equivalent to reductions in cumulative risk, and differences in annualized incidence rates should not be directly converted into conventional NNT estimates. We argue that relative and absolute measures should be presented together. Routine reporting of relative treatment effects, baseline event risks, ARR, and NNT, with a specified time horizon and method of estimation, would improve transparency and clinically meaningful interpretation. This approach would help clinicians, patients, guideline committees, and policymakers understand not only whether a therapy works, but also the magnitude of benefit that may reasonably be expected.
