论文标题
使用贝叶斯的贝叶斯套件进行贝叶斯随机效应元回归
Using the bayesmeta R package for Bayesian random-effects meta-regression
论文作者
论文摘要
背景：层次正常建模框架内的随机效应荟萃分析通常在广泛的证据综合应用中实现。考虑到允许包含研究级别的协变量的元回归方法时，甚至可以解决更多一般的问题。方法：我们描述了贝叶斯式的贝叶斯元回归实现，包括先验的选择，并说明了其实际用途。结果：给出了广泛的示例应用程序，例如二进制和连续的协变量，亚组分析，间接比较和模型选择。提供了示例R代码。结论：贝内米塔软件包提供了灵活的实现。由于避免了MCMC方法，计算是快速且可重复的，可促进快速灵敏度检查或大规模仿真研究。
BACKGROUND: Random-effects meta-analysis within a hierarchical normal modeling framework is commonly implemented in a wide range of evidence synthesis applications. More general problems may even be tackled when considering meta-regression approaches that in addition allow for the inclusion of study-level covariables. METHODS: We describe the Bayesian meta-regression implementation provided in the bayesmeta R package including the choice of priors, and we illustrate its practical use. RESULTS: A wide range of example applications are given, such as binary and continuous covariables, subgroup analysis, indirect comparisons, and model selection. Example R code is provided. CONCLUSIONS: The bayesmeta package provides a flexible implementation. Due to the avoidance of MCMC methods, computations are fast and reproducible, facilitating quick sensitivity checks or large-scale simulation studies.
