Multivariate network meta-analysis to mitigate the effects of outcome reporting bias.
Multivariate network meta-analysis to mitigate the effects of outcome reporting bias.
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DOI：
10.1002/sim.7815
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发表时间：
2018-09-30
影响因子：
2
通讯作者：
DeSantis SM
中科院分区：
文献类型：
--
作者：
Hwang H;DeSantis SM
结果报告偏倚（ORB）被认为是成对和网络荟萃分析（NMA）有效性的威胁。近年来，多变量荟萃分析（MMA）方法已被提出来减少ORB在成对设置中的影响。这些方法表明，MMA可以减少偏倚，提高合并效应量的效率。然而，它是未知的多元NMA（MNMA）是否可以类似地减少ORB。此外，由于必须对治疗和结果之间的相关性进行建模，因此实施MNMA是相当具有挑战性的，因此协方差矩阵的维度和要估计的分量的数量随着治疗的数量和结果的数量而快速增长。为了确定MNMA是否可以减少ORB对合并治疗效应量的影响，我们提出了一个广泛的贝叶斯MNMA模拟研究。通过模拟研究，我们表明，MNMA减少了各种结果缺失情况下的合并效应量的偏倚，包括随机缺失和非随机缺失。此外，MNMA提高了估计的精度，产生更窄的可信区间。我们通过应用MNMA对老年人抑郁症抗抑郁药治疗的随机对照试验进行多治疗系统评价，证明了该方法的适用性。
Outcome reporting bias (ORB) is recognized as a threat to the validity of both pairwise and network meta-analysis (NMA). In recent years, multivariate meta-analysis (MMA) methods have been proposed to reduce the impact of ORB in the pairwise setting. These methods have shown that MMA can reduce bias and increases efficiency of pooled effect sizes. However, it is unknown whether multivariate NMA (MNMA) can similarly reduce ORB. Additionally, it is quite challenging to implement MNMA due to the fact that correlation between treatments and outcomes must be modeled, thus the dimension of the covariance matrix and number of components to estimate grows quickly with the number of treatments and number of outcomes. To determine whether MNMA can reduce the effects of ORB on pooled treatment effect sizes, we present an extensive simulation study of a Bayesian MNMA. Via simulation studies, we show that MNMA reduces the bias of pooled effect sizes under a variety of outcome missingness scenarios, including missing at random and missing not at random. Further, MNMA improves the precision of estimates, producing narrower credible intervals. We demonstrate the applicability of the approach via application of MNMA to a multi-treatment systematic review of randomized controlled trials of anti-depressants for the treatment of depression in older adults.
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影响因子：
1.9
作者：
Jackson D;Bujkiewicz S;Law M;Riley RD;White IR
通讯作者：
White IR
影响因子：
2
作者：
Efthimiou, Orestis;Mavridis, Dimitris;Salanti, Georgia
通讯作者：
Salanti, Georgia
影响因子：
9.8
作者：
Dias, S.;Ades, A. E.
通讯作者：
Ades, A. E.
影响因子：
3.6
作者：
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通讯作者：
Hippisley-Cox, J.
影响因子：
3.6
作者：
DeSantis, Stacia M.;Zhu, Huirong
通讯作者：
Zhu, Huirong