用贝叶斯方法重新评估剖宫产后试产的疗效，提升医疗决策可靠性。
Bayesian Meta-Analyses Could Be More: A Case Study in Trial of Labor After a Cesarean-section Outcomes and Complications
- 引入贝叶斯框架处理未观测变量带来的效应偏差
- 在剖宫产后试产场景中验证了阳性效果的稳健性
- 为产科医生提供更可信的决策支持工具
元分析的有效性依赖于前期研究对关键变量的准确捕捉，但在医学研究中，影响医生决策的关键变量常被遗漏，导致效应量未知且结论不可靠。本文提出一种贝叶斯方法，用于评估此类常见医疗情境下正向效应声明是否仍成立。以剖宫产后试产（TOLAC）为例，该场景中可用干预手段有限，我们协助专业妇产科医生评估患者情况，为临床决策提供坚实支持，推动患者照护进步。
原文摘要 · Abstract (English)
The meta-analysis's utility is dependent on previous studies having accurately captured the variables of interest, but in medical studies, a key decision variable that impacts a physician's decisions was not captured. This results in an unknown effect size and unreliable conclusions. A Bayesian approach may allow analysis to determine if the claim of a positive effect is still warranted, and we build a Bayesian approach to this common medical scenario. To demonstrate its utility, we assist professional OBGYNs in evaluating Trial of Labor After a Cesarean-section (TOLAC) situations where few interventions are available for patients and find the support needed for physicians to advance patient care.
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