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A Bayesian reevaluation of randomized controlled trials in assisted reproductive technology: quantifying evidence
Jiayi Gao1, Tian Tian1, Kalbinur Kayimu1
1State Key Laboratory of Female Fertility Promotion, Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital; National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital); Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education; Beijing Key Laboratory of Collaborative Innovation in Frontier Technologies for Population Quality, Beijing, China.
Introduction:
Many randomized controlled trials (RCTs) in assisted reproductive technology (ART) face widespread misinterpretation of statistically non-significant results within the conventional frequentist framework. This study aimed to reevaluate high-quality ART RCTs using Bayesian methods to quantify the strength of the evidence for both the null and alternative hypotheses.
Methods:
We systematically searched ART-related RCTs published in JAMA, The Lancet, The BMJ, and NEJM between 2010 and 2025. A total of 35 trials were ultimately included for Bayesian reanalysis, which used Bayes factors (BF₁₀) to evaluate the primary outcomes. Sensitivity analyses were performed to confirm the robustness of our primary findings.
Results:
The Bayesian and frequentist results were consistent in 85.7% of the studies. Among these consistent results, 62.9% supported the null hypothesis and 22.9% supported the alternative hypothesis. In 11.4% of the studies, non-significant P-values were paired with inconclusive Bayes factors, indicating data insensitivity. Another 2.9% showed significant P-values but inconclusive BF10. Overall, 71.4% of studies reported non-significant primary outcomes, with an increasing trend observed over time.
Conclusion:
Bayesian analysis offers a useful framework for interpreting "negative results" in ART RCTs. It effectively complements traditional statistics to improve the interpretation of evidence and inform future trial design in ART.
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