Using individual participant data to improve network meta-analysis projects.
Using individual participant data to improve network meta-analysis projects.
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DOI：
10.1136/bmjebm-2022-111931
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发表时间：
2023-06
影响因子：
5.8
通讯作者：
Phillippo, David M.
中科院分区：
文献类型：
--
作者：
Riley, Richard D.;Dias, Sofia;Donegan, Sarah;Tierney, Jayne F.;Stewart, Lesley A.;Efthimiou, Orestis;Phillippo, David M.
一项网络荟萃分析结合了现有随机试验中关于多种治疗效果比较的证据。它允许在同一分析中包含每次比较的直接和间接证据，并提供了一个连贯的框架来比较和排名治疗。传统的网络荟萃分析使用从出版物或试验研究者获得的汇总数据（例如，治疗效果估计值和标准误）。另一种方法是获取、检查、协调和荟萃分析每项试验的个体参与者数据（IPD）。在本文中，我们描述了IPD在网络荟萃分析项目中的潜在优势，强调了五个关键优势：（1）提高纳入荟萃分析的信息的质量和范围，（2）检查并绘制试验间协变量的分布图（例如，对于潜在的效应调节剂），（3）标准化和改进每个试验的分析，（4）调整预后因素，以允许对条件性治疗效应进行网络荟萃分析;（5）包括治疗-协变量相互作用（效应修饰符），以允许相对治疗效应随参与者水平协变量值（例如，年龄、基线抑郁评分）而变化。所有这些益处的一个主题是，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、可靠和信息丰富的结果，甚至可以根据患者的特定特征对个体患者和目标人群进行治疗比较。
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.
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