Testing moderation in network meta-analysis with individual participant data.
Testing moderation in network meta-analysis with individual participant data.
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DOI：
10.1002/sim.6883
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发表时间：
2016-07-10
影响因子：
2
通讯作者：
Liu L
中科院分区：
文献类型：
--
作者：
Dagne GA;Brown CH;Howe G;Kellam SG;Liu L
综合多项干预试验数据的荟萃分析方法通常用于评估干预措施的有效性。它们也可以扩展到研究比较有效性，测试几种替代干预措施中哪一种预计效果最强。这通常需要网络荟萃分析（NMA），它将涉及同一试验中两种干预措施的直接比较和试验间的间接比较的试验结合起来。在本文中，我们扩展了现有的网络方法的主要影响，检查主持人的影响，允许测试是否干预效果不同的人群或在不同的情况下使用。此外，我们研究了如何使用个人参与者数据（IPD）可能会增加NMA检测主持人效应的敏感性，相比，在荟萃回归框架中采用研究级效应大小的汇总数据NMA。提出了一种新的网络元分析图。我们还开发了一个广义的多水平模型NMA，考虑到试验内和试验间的异质性，并可以包括参与者水平的协变量。在此框架内，我们提出了试验间同质性和一致性的定义。基于该模型的模拟研究用于评估对功率的影响，以检测主效应和调节效应。结果表明，功率检测温和的是大得多，当应用到IPD相比，研究水平的影响。我们通过将其应用于一项基于课堂的随机研究的数据来说明这种方法的使用，该研究涉及两个子试验，每个子试验比较与单独对照组对比的干预措施。
Meta-analytic methods for combining data from multiple intervention trials are commonly used to estimate the effectiveness of an intervention. They can also be extended to study comparative effectiveness, testing which of several alternative interventions is expected to have the strongest effect. This often requires network meta-analysis (NMA), which combines trials involving direct comparison of two interventions within the same trial and indirect comparisons across trials. In this paper, we extend existing network methods for main effects to examining moderator effects, allowing for tests of whether intervention effects vary for different populations or when employed in different contexts. In addition, we study how the use of individual participant data (IPD) may increase the sensitivity of NMA for detecting moderator effects, as compared to aggregate data NMA that employs study-level effect sizes in a meta-regression framework. A new network meta-analysis diagram is proposed. We also develop a generalized multilevel model for NMA that takes into account within- and between-trial heterogeneity, and can include participant-level covariates. Within this framework we present definitions of homogeneity and consistency across trials. A simulation study based on this model is used to assess effects on power to detect both main and moderator effects. Results show that power to detect moderation is substantially greater when applied to IPD as compared to study-level effects. We illustrate the use of this method by applying it to data from a classroom-based randomized study that involved two sub-trials, each comparing interventions that were contrasted with separate control groups.