Reproductive medicine is imbued with debates over the results of key trials. This has resulted in heterogeneity in clinical practice and a disconnect between researchers and the patient group they aim to treat. The criticisms of trials originate from the nature of reproductive health conditions and limitations imposed in designing trials to assess effect in a patient group with heterogenous pathologies leading to the same condition. This leads to challenges in balancing the difficulties of recruiting an enriched patient cohort versus the dilutionary effect and need for subgroup analysis from wider recruitment. These challenges manifest as a failure to achieve traditional statistical significance. One potential solution to overcoming these inherent challenges is that of a Bayesian statistical approach. Using examples from the literature we demonstrate the benefits of a Bayesian approach. Taking published data and using a flat prior (no background information used), a Bayesian re-analysis of the PRISM and EAGeR trials is presented. This demonstrated a 94.7% chance of progesterone and a 95.3% probability of aspirin preventing miscarriage, in contrast to the original trial conclusions. These highlight the role a Bayesian approach can play in overcoming the challenges of trials within reproductive health.

生殖医学充满了对关键试验结果的争论。这导致了临床实践中的异质性，以及研究人员与他们旨在治疗的患者群体之间的脱节。对试验的批评源于生殖健康状况的性质和设计试验时强加的限制，以评估具有导致相同状况的异质病理的患者组的效果。这导致在平衡招募丰富的患者队列的困难与稀释效应以及从更广泛的招募中进行亚组分析的需要方面面临挑战。这些挑战表现为未能实现传统的统计意义。克服这些固有挑战的一种潜在解决方案是贝叶斯统计方法。我们使用文献中的例子展示了贝叶斯方法的好处。使用已发布的数据并使用平坦的先验（未使用背景信息），呈现了对 PRISM 和 EAGeR 试验的贝叶斯再分析。与最初的试验结论相反，这证明了 94.7% 的黄体酮机会和 95.3% 的阿司匹林预防流产的可能性。这些突出了贝叶斯方法在克服生殖健康试验挑战方面可以发挥的作用。