Wong Gordon C, Huang Cynthia, Fahmy Joseph N, Zhang Casey, Teunis Teun, Chung Kevin C
From the Section of Plastic Surgery, Department of Surgery, University of Michigan Medical School, Ann Arbor, MI.
Department of Plastic Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA.
Plast Reconstr Surg Glob Open. 2024 Dec 13;12(12):e6370. doi: 10.1097/GOX.0000000000006370. eCollection 2024 Dec.
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 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. 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 value was independently associated with a larger Bayes factor (β = 2.6, <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.
统计学上无显著意义的随机临床试验（RCT）结果难以解释，因为它们无法证明治疗组之间不存在差异。贝叶斯分析提供了另一种统计框架，能够全面理解无显著意义的结果。
这项横断面研究对2013年至2022年发表的RCT中统计学上无显著意义的结果进行了事后贝叶斯分析。计算并检验了代表无差异概率或无差异零假设的贝叶斯因子。还比较了这些结果的P值和贝叶斯因子与其关联性评估。
在73项研究中的176个统计学上无显著意义的结果中，160个（91%）表明无差异的证据（贝叶斯因子>1）。其中110个（63%）的贝叶斯因子在1至3之间，表明无差异的证据较弱；16个（9.1%）结果支持存在差异（贝叶斯因子<1）。更大的P值与更大的贝叶斯因子独立相关（β = 2.6，P<0.001）。
近三分之二无显著意义的RCT结果仅提供了支持无差异的微弱证据。这种不确定性给临床决策带来了挑战，并凸显了资源利用的低效性。将贝叶斯统计纳入未来的试验设计和分析中可以克服这些挑战，提高结果的可解释性，并指导医学实践和研究。
