Bayesian bivariate meta-analysis of correlated effects: Impact of the prior distributions on the between-study correlation, borrowing of strength, and joint inferences.
Bayesian bivariate meta-analysis of correlated effects: Impact of the prior distributions on the between-study correlation, borrowing of strength, and joint inferences.
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DOI：
10.1177/0962280216631361
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发表时间：
2018-03
影响因子：
2.3
通讯作者：
Riley RD
中科院分区：
文献类型：
--
作者：
Burke DL;Bujkiewicz S;Riley RD
多变量随机效应荟萃分析允许联合合成来自多个研究的相关结果，例如，多个结果或多个治疗组。在一个终点的贝叶斯单变量荟萃分析中，为研究间方差指定合理的先验分布的重要性是很好理解的。然而，在多变量Meta分析中，对于方差或关键的研究间相关性ρB的先验分布的选择几乎没有指导；对于后者，研究人员经常使用均匀(−1，1)分布，假设它是模糊的。在这篇文章中，一个广泛的模拟研究和一个真实的说明性例子被用来检验ρB的各种(现实的)模糊先验分布的影响，以及两个相关处理效应的贝叶斯双变量随机效应荟萃分析中研究间方差的影响。考虑了一系列不同的情况，包括完整和缺失的数据，以检查先验分布对新研究中的后验结果(治疗效果和研究间相关性)、借用强度的量以及治疗效果的联合预测分布的影响。确定了两个关键建议，以提高多变量荟萃分析结果的稳健性。首先，如果可能，应避免对−B常规使用统一(ρ1，1)先验分布，因为它不一定是模糊的。相反，研究人员应该确定合理的先验分布，例如，根据先验知识将值限制为正值或负值。其次，对研究间方差使用合理的(例如基于经验的)先验分布仍然至关重要，因为不适当的选择可能会对ρB的后验分布产生不利影响，从而可能对联合预测概率等推断产生不利影响。在少数研究和缺失数据的情况下，这些建议尤其重要。
Multivariate random-effects meta-analysis allows the joint synthesis of correlated results from multiple studies, for example, for multiple outcomes or multiple treatment groups. In a Bayesian univariate meta-analysis of one endpoint, the importance of specifying a sensible prior distribution for the between-study variance is well understood. However, in multivariate meta-analysis, there is little guidance about the choice of prior distributions for the variances or, crucially, the between-study correlation, ρB; for the latter, researchers often use a Uniform(−1,1) distribution assuming it is vague. In this paper, an extensive simulation study and a real illustrative example is used to examine the impact of various (realistically) vague prior distributions for ρB and the between-study variances within a Bayesian bivariate random-effects meta-analysis of two correlated treatment effects. A range of diverse scenarios are considered, including complete and missing data, to examine the impact of the prior distributions on posterior results (for treatment effect and between-study correlation), amount of borrowing of strength, and joint predictive distributions of treatment effectiveness in new studies. Two key recommendations are identified to improve the robustness of multivariate meta-analysis results. First, the routine use of a Uniform(−1,1) prior distribution for ρB should be avoided, if possible, as it is not necessarily vague. Instead, researchers should identify a sensible prior distribution, for example, by restricting values to be positive or negative as indicated by prior knowledge. Second, it remains critical to use sensible (e.g. empirically based) prior distributions for the between-study variances, as an inappropriate choice can adversely impact the posterior distribution for ρB, which may then adversely affect inferences such as joint predictive probabilities. These recommendations are especially important with a small number of studies and missing data.
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影响因子：
2
作者：
Bujkiewicz S;Thompson JR;Riley RD;Abrams KR
通讯作者：
Abrams KR
影响因子：
2.2
作者：
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通讯作者：
Perneger, Thomas V.
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2
作者：
Pullenayegum, Eleanor M.
通讯作者：
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作者：
Nam, IS;Mengersen, K;Garthwaite, P
通讯作者：
Garthwaite, P
影响因子：
2
作者：
Riley, R. D.;Abrams, K. R.;Thompson, J. R.
通讯作者：
Thompson, J. R.