Riley R D, Price M J, Jackson D, Wardle M, Gueyffier F, Wang J, Staessen J A, White I R
Research Institute of Primary Care and Health Sciences, Keele University, Staffordshire, ST5 5BG, UK.
School of Health and Population Sciences, Public Health Building, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
Res Synth Methods. 2015 Jun;6(2):157-74. doi: 10.1002/jrsm.1129. Epub 2014 Nov 21.
When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models.
在合并相关研究的结果时，多变量荟萃分析允许对来自多个结局的相关效应估计值进行联合综合。联合综合比单独的单变量综合更有效率，可能减少选择性结局报告偏倚，并能对各结局进行联合推断。一个常见的问题是，从已发表的报告中无法得知拟合多变量模型所需的研究内相关性。然而，提供个体参与者数据（IPD）可使其直接计算出来。在此，我们举例说明如何使用IPD来估计研究内相关性，对于多个连续结局使用联合线性回归，对于二元、生存和混合结局使用自助法。在一项对10项高血压试验的荟萃分析中，我们接着展示这些方法如何使多变量荟萃分析能够解决关于连续、生存和二元结局；治疗协变量相互作用；调整后的风险/预后因素效应；纵向数据；预后和多参数模型；以及多种治疗比较的新临床问题。同时应用了频率学派和贝叶斯方法，并提供了示例软件代码以推导研究内相关性并拟合模型。
