Department of Statistics, Florida State University, Tallahassee, FL, 32306, USA.
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, 55455, USA.
Res Synth Methods. 2018 Jun;9(2):261-272. doi: 10.1002/jrsm.1293. Epub 2018 Mar 24.
In medical sciences, a disease condition is typically associated with multiple risk and protective factors. Although many studies report results of multiple factors, nearly all meta-analyses separately synthesize the association between each factor and the disease condition of interest. The collected studies usually report different subsets of factors, and the results from separate analyses on multiple factors may not be comparable because each analysis may use different subpopulation. This may impact on selecting most important factors to design a multifactor intervention program. This article proposes a new concept, multivariate meta-analysis of multiple factors (MVMA-MF), to synthesize all available factors simultaneously. By borrowing information across factors, MVMA-MF can improve statistical efficiency and reduce biases compared with separate analyses when factors were missing not at random. As within-study correlations between factors are commonly unavailable from published articles, we use a Bayesian hybrid model to perform MVMA-MF, which effectively accounts for both within- and between-study correlations. The performance of MVMA-MF and the conventional methods are compared using simulations and an application to a pterygium dataset consisting of 29 studies on 8 risk factors.
在医学科学中，疾病状况通常与多种风险和保护因素有关。尽管许多研究报告了多种因素的结果，但几乎所有的荟萃分析都分别综合了每个因素与感兴趣的疾病状况之间的关联。所收集的研究通常报告了不同的因素子集，并且对多个因素进行单独分析的结果可能无法比较，因为每个分析可能使用不同的亚群。这可能会影响选择最重要的因素来设计多因素干预计划。本文提出了一个新概念，即多因素的多元荟萃分析（MVMA-MF），以同时综合所有可用的因素。通过跨因素借用信息，与单独分析相比，MVMA-MF 可以在因素不是随机缺失时提高统计效率并减少偏差。由于发表的文章中通常无法获得因素之间的内部相关性，因此我们使用贝叶斯混合模型来进行 MVMA-MF，该模型有效地考虑了内部和研究之间的相关性。使用模拟和对包含 8 个风险因素的 29 项研究的翼状胬肉数据集的应用，比较了 MVMA-MF 和常规方法的性能。
