Bayesian model-averaged meta-analysis allows quantification of evidence for both treatment effectiveness $\mu$ and across-study heterogeneity $\tau$. We use the Cochrane Database of Systematic Reviews to develop discipline-wide empirical prior distributions for $\mu$ and $\tau$ for meta-analyses of binary and time-to-event clinical trial outcomes. First, we use 50% of the database to estimate parameters of different required parametric families. Second, we use the remaining 50% of the database to select the best-performing parametric families and explore essential assumptions about the presence or absence of the treatment effectiveness and across-study heterogeneity in real data. We find that most meta-analyses of binary outcomes are more consistent with the absence of the meta-analytic effect or heterogeneity while meta-analyses of time-to-event outcomes are more consistent with the presence of the meta-analytic effect or heterogeneity. Finally, we use the complete database - with close to half a million trial outcomes - to propose specific empirical prior distributions, both for the field in general and for specific medical subdisciplines. An example from acute respiratory infections demonstrates how the proposed prior distributions can be used to conduct a Bayesian model-averaged meta-analysis in the open-source software R and JASP.

翻译：贝叶斯模型平均元分析能够量化治疗效果μ及研究间异质性τ的证据。我们利用Cochrane系统评价数据库，为二分类和时间至事件临床试验结局的元分析构建学科层面的经验先验分布。首先，使用50%的数据库估计不同参数族所需参数；其次，利用剩余50%的数据库筛选表现最佳的参数族，并探究真实数据中治疗效果存在与否及研究间异质性的基本假设。研究发现，大多数二分类结局的元分析更符合无元分析效应或异质性的情况，而时间至事件结局的元分析则更符合存在元分析效应或异质性的情况。最后，我们利用包含近50万项试验结局的完整数据库，提出适用于整个学科领域及特定医学分支的经验先验分布。通过急性呼吸道感染实例，展示如何在开源软件R和JASP中利用所提出的先验分布进行贝叶斯模型平均元分析。