School of Health and Related Research, University of Sheffield, Sheffield, England, UK.
School of Mathematics and Statistics, University of Sheffield, Sheffield, England, UK.
Med Decis Making. 2018 May;38(4):531-542. doi: 10.1177/0272989X18759488. Epub 2018 Mar 29.
Pairwise and network meta-analyses using fixed effect and random effects models are commonly applied to synthesize evidence from randomized controlled trials. The models differ in their assumptions and the interpretation of the results. The model choice depends on the objective of the analysis and knowledge of the included studies. Fixed effect models are often used because there are too few studies with which to estimate the between-study SD from the data alone.
The aim of this study was to propose a framework for eliciting an informative prior distribution for the between-study SD in a Bayesian random effects meta-analysis model to genuinely represent heterogeneity when data are sparse.
We developed an elicitation method using external information, such as empirical evidence and expert beliefs, on the "range" of treatment effects to infer the prior distribution for the between-study SD. We also developed the method to be implemented in R.
The 3-stage elicitation approach allows uncertainty to be represented by a genuine prior distribution to avoid making misleading inferences. It is flexible to what judgments an expert can provide and is applicable to all types of outcome measures for which a treatment effect can be constructed on an additive scale.
The choice between using a fixed effect or random effects meta-analysis model depends on the inferences required and not on the number of available studies. Our elicitation framework captures external evidence about heterogeneity and overcomes the assumption that studies are estimating the same treatment effect, thereby improving the quality of inferences in decision making.
使用固定效应和随机效应模型的成对和网络荟萃分析常用于综合随机对照试验的证据。这些模型在假设和结果解释方面存在差异。模型的选择取决于分析的目的和对纳入研究的了解。由于单独从数据中估计研究间标准差的研究太少，因此通常使用固定效应模型。
本研究旨在提出一种在贝叶斯随机效应荟萃分析模型中为研究间 SD eliciting 一个信息先验分布的框架，以在数据稀疏时真正表示异质性。
我们开发了一种使用外部信息（如经验证据和专家意见）的启发式方法，对“治疗效果范围”进行启发式方法，以推断研究间 SD 的先验分布。我们还开发了一种在 R 中实现的方法。
3 阶段启发式方法允许通过真实的先验分布来表示不确定性，以避免产生误导性的推断。它灵活地适应专家可以提供的判断，并且适用于所有类型的可以在加性尺度上构建治疗效果的结果测量。
选择使用固定效应或随机效应荟萃分析模型取决于所需的推断，而不是可用研究的数量。我们的启发式框架捕捉了关于异质性的外部证据，并克服了研究估计相同治疗效果的假设，从而提高了决策中的推断质量。
