Mixed treatment comparisons using aggregate and individual participant level data
Mixed treatment comparisons using aggregate and individual participant level data
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DOI：
10.1002/sim.5442
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发表时间：
2012-12-10
影响因子：
2
通讯作者：
Manca, Andrea
中科院分区：
文献类型：
--
作者：
Saramago, Pedro;Sutton, Alex J.;Manca, Andrea
混合治疗比较(MTC)扩展了传统的成对荟萃分析框架，以综合关于两种以上干预措施的信息。尽管大多数MTC使用聚合数据(AD)，但可以在个人一级获得一定比例的证据库。我们开发了一系列新的贝叶斯统计MTC模型，以允许同时合成IPD和AD，潜在地结合了研究和个体水平的协变量。不同干预措施的有效性被用作激励数据集，以增加在有儿童的家庭中提供有效的烟雾报警器。这包括20项研究(11项AD和9项IPD)，包括11 500名参与者。将IPD纳入网络允许包括关于受试者水平协变量的信息，这比从所有研究中仅对AD进行分析产生的处理和协变量交互作用估计明显更准确。在探索参与者水平的协变量时，在MTC中纳入IPD水平的证据是可取的；即使IPD只在一小部分研究中可用。这样的模型不仅可以减少试验网络中的不一致，而且还有助于估计干预亚组的影响，以指导更个性化的治疗决定。版权所有(C)2012 John Wiley&Sons，Ltd.
Mixed treatment comparisons (MTC) extend the traditional pair-wise meta-analytic framework to synthesize information on more than two interventions. Although most MTCs use aggregate data (AD), a proportion of the evidence base might be available at the individual level (IPD). We develop a series of novel Bayesian statistical MTC models to allow for the simultaneous synthesis of IPD and AD, potentially incorporating study and individual level covariates. The effectiveness of different interventions to increase the provision of functioning smoke alarms in households with children was used as a motivating dataset. This included 20 studies (11 AD and 9 IPD), including 11 500 participants. Incorporating the IPD into the network allowed the inclusion of information on subject level covariates, which produced markedly more accurate treatmentcovariate interaction estimates than an analysis solely on the AD from all studies. Including evidence at the IPD level in the MTC is desirable when exploring participant level covariates; even when IPD is available only for a fraction of the studies. Such modelling may not only reduce inconsistencies within networks of trials but also assist the estimation of intervention subgroup effects to guide more individualised treatment decisions. Copyright (C) 2012 John Wiley & Sons, Ltd.