Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
Department of Pediatric Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.
Stat Med. 2021 Dec 30;40(30):6743-6761. doi: 10.1002/sim.9170. Epub 2021 Oct 27.
We outline a Bayesian model-averaged (BMA) meta-analysis for standardized mean differences in order to quantify evidence for both treatment effectiveness and across-study heterogeneity . We construct four competing models by orthogonally combining two present-absent assumptions, one for the treatment effect and one for across-study heterogeneity. To inform the choice of prior distributions for the model parameters, we used 50% of the Cochrane Database of Systematic Reviews to specify rival prior distributions for and . The relative predictive performance of the competing models and rival prior distributions was assessed using the remaining 50% of the Cochrane Database. On average, -the model that assumes the presence of a treatment effect as well as across-study heterogeneity-outpredicted the other models, but not by a large margin. Within , predictive adequacy was relatively constant across the rival prior distributions. We propose specific empirical prior distributions, both for the field in general and for each of 46 specific medical subdisciplines. An example from oral health demonstrates how the proposed prior distributions can be used to conduct a BMA meta-analysis in the open-source software R and JASP. The preregistered analysis plan is available at https://osf.io/zs3df/.
我们概述了一种贝叶斯模型平均（BMA）荟萃分析，用于标准化均数差，以量化治疗效果和研究间异质性的证据。我们通过正交组合两种存在-不存在假设来构建四个竞争模型，一种用于治疗效果，一种用于研究间异质性。为了为模型参数选择先验分布，我们使用 Cochrane 系统评价数据库的 50%来指定 和 的竞争先验分布。使用 Cochrane 数据库的剩余 50%评估竞争模型和竞争先验分布的相对预测性能。平均而言，-假设存在治疗效果和研究间异质性的模型-优于其他模型，但优势不大。在 内，预测充分性在竞争先验分布之间相对稳定。我们为一般领域和 46 个特定医学子领域中的每一个都提出了具体的经验先验分布。口腔健康的一个例子演示了如何在开源软件 R 和 JASP 中使用建议的先验分布进行 BMA 荟萃分析。预先注册的分析计划可在 https://osf.io/zs3df/ 获得。
