Beyond Relative Risk: A Methodological Framework for Interpreting Measures of Effect and Improving Data Presentation in Randomized Controlled Trials (RCTs)

Randomized controlled trials (RCTs) are the gold standard for evaluating the efficacy and safety of medical interventions. However, the interpretation of their results is often obscured by an overreliance on relative measures of effect, such as relative risk reduction (RRR) and hazard ratios (HRs). While statistically robust, these measures may mislead clinicians and patients when used in isolation. This article provides a methodological framework for the comprehensive interpretation of treatment effects in RCTs, emphasizing the importance of integrating absolute measures such as absolute risk reduction (ARR), number needed to treat (NNT), annualized NNT (aNNT), and number needed to harm (NNH). Additionally, we explore the conceptual differences between risk-based and rate-based measures, the clinical implications of time-to-event analyses, and the utility of composite metrics such as the likelihood of being helped or harmed (LHH). By adopting a multidimensional approach to effect estimation, researchers and clinicians can enhance the translation of statistical findings into meaningful clinical decisions. This approach also facilitates communication with patients.