Bayesian Reanalysis of Statistically Nonsignificant Outcomes in Plastic Surgery Clinical Trials

Statistically nonsignificant randomized clinical trial (RCT) results are challenging to interpret, as they are unable to prove the absence of a difference between treatment groups. Bayesian analysis offers an alternative statistical framework capable of providing a comprehensive understanding of nonsignificant results.

This cross-sectional study conducted a post hoc Bayesian analysis of statistically nonsignificant outcomes from RCTs published in Plastic and Reconstructive Surgery from 2013 to 2022. Bayes factors representing the probability of the absence of a difference, or the null hypothesis of no difference, were calculated and examined. P values and Bayes factors of these outcomes were also compared with assessment of their association.

In 73 studies with 176 statistically nonsignificant outcomes, 160 (91%) indicated evidence for the absence of a difference (Bayes factor > 1). For 110 (63%) of these, the Bayes factor was between 1 and 3, indicating weak evidence for the absence of a difference; 16 (9.1%) results supported the presence of a difference (Bayes factor < 1). A greater P value was independently associated with a larger Bayes factor (β = 2.6, P <0.001).

Nearly two-thirds of nonsignificant RCT outcomes provided only weak evidence supporting the absence of a difference. This uncertainty poses challenges for clinical decision-making and highlights the inefficiency in resource utilization. Integrating Bayesian statistics into future trial design and analysis could overcome these challenges, enhancing result interpretability and guiding medical practice and research.