论文标题
荟萃分析（MEMA）中的测量误差 - 连续结果数据的贝叶斯框架
Measurement Error in Meta-Analysis (MEMA) -- a Bayesian framework for continuous outcome data
论文作者
论文摘要
理想情况下，荟萃分析将总结一些无偏研究的数据。在这里，我们认为，在测量误差中可能会损害贡献研究的理想情况较小。测量误差会影响每个研究设计，从随机对照试验到回顾性观察研究。我们概述了连续结果数据的灵活的贝叶斯框架，该框架允许一个人获得有关测量误差大小的先验知识，获得适当的点和间隔估计值。我们还展示了如果有个人参与数据（IPD）可用，贝叶斯荟萃分析模型可以针对多个参与者级别的协变量进行调整，该协变量以或没有测量误差为单位。
Ideally, a meta-analysis will summarize data from several unbiased studies. Here we consider the less than ideal situation in which contributing studies may be compromised by measurement error. Measurement error affects every study design, from randomized controlled trials to retrospective observational studies. We outline a flexible Bayesian framework for continuous outcome data which allows one to obtain appropriate point and interval estimates with varying degrees of prior knowledge about the magnitude of the measurement error. We also demonstrate how, if individual-participant data (IPD) are available, the Bayesian meta-analysis model can adjust for multiple participant-level covariates, measured with or without measurement error.
