Network meta-analysis of individual and aggregate level data
Network meta-analysis of individual and aggregate level data
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DOI：
10.1002/jrsm.1048
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发表时间：
2012-06-01
影响因子：
9.8
通讯作者：
Jansen, Jeroen P.
中科院分区：
文献类型：
--
作者：
Jansen, Jeroen P.
网络荟萃分析通常使用聚合级数据（AgD）进行。使用AgD的一个挑战是，患者水平协变量与研究水平治疗效应之间的相关性可能不反映个体水平效应修正。在本文中，非线性网络荟萃分析模型结合个体患者数据（IPD）和AgD的存在异质性，以减少偏倚和不确定性的直接和间接的治疗效果。第一种方法对IPD和AgD使用相同的模型形式。采用第二种方法，通过整合协变量联合研究内分布的基础IPD模型获得AgD模型，与杰克逊等人的生态学推断方法一致。通过仿真实例对模型进行了说明。在存在患者水平异质性的情况下，对研究子集进行IPD可改善对治疗效果的估计。在两个拟议的非线性模型结合IPD和AgD，第二个似乎受偏倚的影响较小的情况下，大治疗患者水平的协变量相互作用，可能是在更大的不确定性的成本。需要更多的研究来更好地了解何时一种模型优于另一种模型。对于网络荟萃分析，建议在可用时使用IPD。版权所有（C）2012约翰威利父子有限公司
Network meta-analysis is often performed with aggregate-level data (AgD). A challenge in using AgD is that the association between a patient-level covariate and treatment effects at the study level may not reflect the individual-level effect modification. In this paper, non-linear network meta-analysis models for combining individual patient data (IPD) and AgD are presented to reduce bias and uncertainty of direct and indirect treatment effects in the presence of heterogeneity. The first method uses the same model form for IPD and AgD. With the second method, the model for AgD is obtained by integrating an underlying IPD model over the joint within-study distribution of covariates, in line with the method by Jackson et al. for ecological inferences. With simulated examples, the models are illustrated. Having IPD for a subset of studies improves estimation of treatment effects in the presence of patient-level heterogeneity. Of the two proposed nonlinear models for combining IPD and AgD, the second seems less affected by bias in situations with large treatment-by-patient-level-covariate interactions, probably at the cost of greater uncertainty. Additional studies are needed to better understand when one model is favorable over the other. For network meta-analysis, it is recommended to use IPD when available. Copyright (C) 2012 John Wiley & Sons, Ltd.