School of Medicine, Keele University, Keele, UK
Centre for Reviews and Dissemination, University of York, York, UK.
BMJ Evid Based Med. 2023 Jun;28(3):197-203. doi: 10.1136/bmjebm-2022-111931. Epub 2022 Aug 10.
A network meta-analysis combines the evidence from existing randomised trials about the comparative efficacy of multiple treatments. It allows direct and indirect evidence about each comparison to be included in the same analysis, and provides a coherent framework to compare and rank treatments. A traditional network meta-analysis uses aggregate data (eg, treatment effect estimates and standard errors) obtained from publications or trial investigators. An alternative approach is to obtain, check, harmonise and meta-analyse the individual participant data (IPD) from each trial. In this article, we describe potential advantages of IPD for network meta-analysis projects, emphasising five key benefits: (1) improving the quality and scope of information available for inclusion in the meta-analysis, (2) examining and plotting distributions of covariates across trials (eg, for potential effect modifiers), (3) standardising and improving the analysis of each trial, (4) adjusting for prognostic factors to allow a network meta-analysis of conditional treatment effects and (5) including treatment-covariate interactions (effect modifiers) to allow relative treatment effects to vary by participant-level covariate values (eg, age, baseline depression score). A running theme of all these benefits is that they help examine and reduce heterogeneity (differences in the true treatment effect between trials) and inconsistency (differences in the true treatment effect between direct and indirect evidence) in the network. As a consequence, an IPD network meta-analysis has the potential for more precise, reliable and informative results for clinical practice and even allows treatment comparisons to be made for individual patients and targeted populations conditional on their particular characteristics.
网络荟萃分析将来自现有随机试验的关于多种治疗方法比较疗效的证据结合起来。它允许将关于每种比较的直接证据和间接证据纳入同一分析，并提供了一个连贯的框架来比较和排序治疗方法。传统的网络荟萃分析使用从出版物或试验研究者那里获得的汇总数据（例如，治疗效果估计值和标准误差）。另一种方法是从每个试验中获取、检查、协调和荟萃分析个体参与者数据（IPD）。在本文中，我们描述了 IPD 对网络荟萃分析项目的潜在优势，强调了五个关键益处：（1）提高了可纳入荟萃分析的信息的质量和范围；（2）检查和绘制跨试验协变量的分布（例如，用于潜在的效应修饰符）；（3）标准化和改进每个试验的分析；（4）调整预后因素以允许对条件治疗效果进行网络荟萃分析；（5）包括治疗-协变量相互作用（效应修饰符），以允许根据参与者水平协变量值（例如年龄、基线抑郁评分）来改变相对治疗效果。所有这些好处的一个共同主题是，它们有助于检查和减少网络中的异质性（试验之间真实治疗效果的差异）和不一致性（直接证据和间接证据之间真实治疗效果的差异）。因此，IPD 网络荟萃分析有可能为临床实践提供更精确、可靠和有用的结果，甚至允许根据特定特征对个体患者和目标人群进行治疗比较。
