Bayesian Reanalysis of Statistically Nonsignificant Outcomes in Plastic Surgery Clinical Trials
Background:. 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 underst...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer
2024-12-01
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| Series: | Plastic and Reconstructive Surgery, Global Open |
| Online Access: | http://journals.lww.com/prsgo/fulltext/10.1097/GOX.0000000000006370 |
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| Summary: | Background:. 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.
Methods:. 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.
Results:. 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).
Conclusions:. 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. |
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| ISSN: | 2169-7574 |