An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios
An approach for modelling multiple correlated outcomes in a network of interventions using odds ratios
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DOI：
10.1002/sim.6117
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发表时间：
2014-06-15
影响因子：
2
通讯作者：
Salanti, Georgia
中科院分区：
文献类型：
--
作者：
Efthimiou, Orestis;Mavridis, Dimitris;Salanti, Georgia
与一系列独立的单变量荟萃分析相比，两个或多个相关结局的多变量荟萃分析有望提高精度，特别是当有研究报告了一些但不是所有结局时。多变量荟萃分析需要估计研究内相关性，这是很少可用的。同时分析多个结果的现有方法仅限于成对治疗比较。我们提出了一个模型的联合，同时合成的多个二分的结果在网络的干预措施，并介绍了一种简单的方法来引出专家意见的研究内的相关性，利用一组条件概率参数。我们在贝叶斯框架内实现了我们的多结果网络元分析模型，该模型允许纳入专家信息。作为一个例子，我们分析了两个相关的二分法的结果，对治疗的反应和辍学率，在网络的药物干预急性躁狂症。与简单的网络荟萃分析相比，所产生的估计值具有更窄的置信区间。我们的结论是，所提出的模型和建议的相关性的事先启发方法构成了一个有用的框架进行网络荟萃分析的多个结果。版权所有（c）2014约翰威利父子有限公司
A multivariate meta-analysis of two or more correlated outcomes is expected to improve precision compared with a series of independent, univariate meta-analyses especially when there are studies reporting some but not all outcomes. Multivariate meta-analysis requires estimates of the within-study correlations, which are seldom available. Existing methods for analysing multiple outcomes simultaneously are limited to pairwise treatment comparisons. We propose a model for a joint, simultaneous synthesis of multiple dichotomous outcomes in a network of interventions and introduce a simple way to elicit expert opinion for the within-study correlations by utilizing a set of conditional probability parameters. We implement our multiple-outcomes network meta-analysis model within a Bayesian framework, which allows incorporation of expert information. As an example, we analyse two correlated dichotomous outcomes, response to the treatment and dropout rate, in a network of pharmacological interventions for acute mania. The produced estimates have narrower confidence intervals compared with the simple network meta-analysis. We conclude that the proposed model and the suggested prior elicitation method for correlations constitute a useful framework for performing network meta-analysis for multiple outcomes. Copyright (c) 2014 John Wiley & Sons, Ltd.