Multivariate meta-analysis using individual participant data.
Multivariate meta-analysis using individual participant data.
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DOI：
10.1002/jrsm.1129
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发表时间：
2015-06
影响因子：
9.8
通讯作者：
White IR
中科院分区：
文献类型：
--
作者：
Riley RD;Price MJ;Jackson D;Wardle M;Gueyffier F;Wang J;Staessen JA;White IR
当结合相关研究的结果时，多变量荟萃分析允许联合合成来自多个结局的相关效应估计。联合综合可以提高效率，单独的单变量综合，可以减少选择性的结果报告偏差，并使联合推断的结果。一个常见的问题是，研究内的相关性需要拟合多变量模型是未知的，从已发表的报告。然而，提供个人参与者数据（IPD）允许直接计算它们。在这里，我们说明了如何使用IPD来估计研究内的相关性，使用联合线性回归的多个连续的结果和二进制，生存和混合结果的自举方法。在对10项高血压试验的荟萃分析中，我们展示了这些方法如何使多变量荟萃分析能够解决有关连续、生存和二元结局的新临床问题;治疗协变量相互作用;调整的风险/预后因素影响;纵向数据;预后和多参数模型;以及多治疗比较。频率论和贝叶斯方法都适用，并提供示例软件代码来推导研究内相关性并拟合模型。
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.