Joint synthesis of multiple correlated outcomes in networks of interventions.
Joint synthesis of multiple correlated outcomes in networks of interventions.
复制标题
干预网络中多个相关结果的联合合成。
DOI：
10.1093/biostatistics/kxu030
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发表时间：
2015-01
期刊：
影响因子：
--
通讯作者：
Salanti G
中科院分区：
文献类型：
--
作者：
Efthimiou O;Mavridis D;Riley RD;Cipriani A;Salanti G
多结果多变量荟萃分析（MOMA）作为一种联合综合证据的工具越来越受欢迎，这些证据来自报告多个相关结果的效果估计的研究。MOMA模型可用于两种治疗方法对多种结局进行成对荟萃分析的情况。网络荟萃分析（NMA）可用于处理比较两种以上治疗的研究;然而，目前很少有指导如何对具有多种结局的干预网络进行MOMA。本文的目的是解决这个问题，提出了两个模型，综合证据多臂研究报告的多个相关结果的网络竞争的治疗。我们的模型可以处理连续的、二元的、事件发生时间或混合的结果，无论是否存在研究内相关性。它们被设置在贝叶斯框架中，以允许在将先验分布拟合和分配给感兴趣的参数时的灵活性，同时充分考虑参数的不确定性。作为一个说明性的例子，我们使用一个网络的干预急性躁狂症，其中包含多臂研究报告两个相关的二元结果：反应率和脱落率。与独立的单变量网络荟萃分析相比，两种多结局NMA模型对每个结局产生的置信区间更窄，并对治疗的相对排名产生影响。
Multiple outcomes multivariate meta-analysis (MOMA) is gaining in popularity as a tool for jointly synthesizing evidence coming from studies that report effect estimates for multiple correlated outcomes. Models for MOMA are available for the case of the pairwise meta-analysis of two treatments for multiple outcomes. Network meta-analysis (NMA) can be used for handling studies that compare more than two treatments; however, there is currently little guidance on how to perform an MOMA for the case of a network of interventions with multiple outcomes. The aim of this paper is to address this issue by proposing two models for synthesizing evidence from multi-arm studies reporting on multiple correlated outcomes for networks of competing treatments. Our models can handle continuous, binary, time-to-event or mixed outcomes, with or without availability of within-study correlations. They are set in a Bayesian framework to allow flexibility in fitting and assigning prior distributions to the parameters of interest while fully accounting for parameter uncertainty. As an illustrative example, we use a network of interventions for acute mania, which contains multi-arm studies reporting on two correlated binary outcomes: response rate and dropout rate. Both multiple-outcomes NMA models produce narrower confidence intervals compared with independent, univariate network meta-analyses for each outcome and have an impact on the relative ranking of the treatments.
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影响因子：
2
作者：
Riley, R. D.;Abrams, K. R.;Thompson, J. R.
通讯作者：
Thompson, J. R.
影响因子：
2
作者：
Efthimiou, Orestis;Mavridis, Dimitris;Salanti, Georgia
通讯作者：
Salanti, Georgia
影响因子：
2.1
作者：
Riley, Richard D.;Thompson, John R.;Abrams, Keith R.
通讯作者：
Abrams, Keith R.
影响因子：
3.7
作者：
Nikolakopoulou A;Chaimani A;Veroniki AA;Vasiliadis HS;Schmid CH;Salanti G
通讯作者：
Salanti G
影响因子：
7.2
作者：
Salanti, Georgia;Ades, A. E.;Ioannidis, John P. A.
通讯作者：
Ioannidis, John P. A.