Authors: Huber M et al.
Source: Anesthesiology, February 03, 2026, 10.1097/ALN.0000000000005914
Summary:
This research letter introduces and illustrates two Bayesian indices designed to complement traditional frequentist analyses in randomized controlled trials in anesthesiology: an index of effect existence and an index of practical significance. The author argues that reliance on P values and confidence intervals alone can obscure clinically meaningful information, particularly when evidence lies near conventional statistical thresholds.
From a Bayesian perspective, treatment effects and hypotheses are treated probabilistically rather than as fixed but unknown quantities. Using the posterior distribution of the estimated treatment effect, Bayesian methods allow direct probability statements about both the direction and magnitude of an effect. The first index discussed, the probability of direction, quantifies the certainty that a treatment effect is either positive or negative, regardless of its size. This index closely mirrors the frequentist P value but offers a more intuitive probabilistic interpretation, focusing on whether an effect exists in a consistent direction.
The second index is based on the region of practical equivalence (ROPE), which defines a range of effect sizes that are considered clinically negligible rather than statistically null. The ROPEfull index represents the proportion of the posterior distribution that lies within this region. A smaller ROPEfull indicates stronger evidence that the treatment effect is not only nonzero but also clinically meaningful.
These indices were applied to outcome data from 52 multicenter randomized controlled anesthesiology trials. Bayesian logistic regression models with weakly informative priors were used, and results were compared with traditional P values. The analysis demonstrated that while the probability of direction closely tracks P values, ROPEfull provides distinct information regarding clinical relevance and is only weakly associated with frequentist significance. Importantly, trials with nearly identical evidence could be dichotomized differently under a strict P < 0.05 framework, whereas Bayesian indices revealed their similarity.
An illustrative example involving intravenous amisulpride for postoperative nausea and vomiting showed that a trial narrowly missing statistical significance nonetheless demonstrated strong Bayesian evidence for both effect existence and practical significance. Overall, the findings emphasize that confidence in the direction of an effect does not necessarily imply confidence in its clinical importance, and vice versa.
The author concludes that Bayesian indices add meaningful interpretive value to anesthesiology trials by shifting the focus from hypothesis testing toward estimation and clinical relevance. Reporting these indices alongside traditional frequentist measures may improve interpretation, avoid arbitrary dichotomization, and better align statistical evidence with clinical decision-making.
Key Points:
• Bayesian methods provide probabilistic interpretations of treatment effects
• The probability of direction quantifies confidence that an effect exists
• ROPEfull assesses whether an effect is likely to be clinically meaningful
• P values and Bayesian indices can diverge near significance thresholds
• Trials with similar evidence may be classified differently by P values alone
• Effect existence and practical significance are related but distinct concepts
• Bayesian indices complement, rather than replace, frequentist analyses
Thank you to Anesthesiology for allowing us to summarize and discuss this letter.