MAPI Consultancy, Tufts University School of Medicine, Boston, MA 02114, USA.
Res Synth Methods. 2012 Jun;3(2):177-90. doi: 10.1002/jrsm.1048.
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 non-linear 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 © 2012 John Wiley & Sons, Ltd.
网络荟萃分析通常使用汇总水平数据（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果之间的关联可能无法反映个体水平的效应修饰。在本文中，提出了用于结合个体患者数据（IPD）和 AgD 的非线性网络荟萃分析模型，以减少异质性存在时直接和间接治疗效果的偏差和不确定性。第一种方法对 IPD 和 AgD 使用相同的模型形式。对于第二种方法，AgD 的模型是通过在协变量的联合研究内分布上整合基础 IPD 模型获得的，这符合 Jackson 等人用于生态推断的方法。通过模拟示例说明了这些模型。对于一组研究的 IPD 子集，可以改善在存在患者水平异质性的情况下对治疗效果的估计。在存在大的治疗-患者水平协变量相互作用的情况下，对于两种用于结合 IPD 和 AgD 的拟非线性模型，第二种模型似乎受偏差的影响较小，可能是以更大的不确定性为代价。需要进一步的研究来更好地理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 © 2012 约翰威立父子有限公司
