Two-stage or not two-stage? That is the question for IPD meta-analysis projects

IF 5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Research Synthesis Methods Pub Date : 2023-08-22 DOI:10.1002/jrsm.1661
Richard D. Riley, Joie Ensor, Miriam Hattle, Katerina Papadimitropoulou, Tim P. Morris
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引用次数: 1

Abstract

Individual participant data meta-analysis (IPDMA) projects obtain, check, harmonise and synthesise raw data from multiple studies. When undertaking the meta-analysis, researchers must decide between a two-stage or a one-stage approach. In a two-stage approach, the IPD are first analysed separately within each study to obtain aggregate data (e.g., treatment effect estimates and standard errors); then, in the second stage, these aggregate data are combined in a standard meta-analysis model (e.g., common-effect or random-effects). In a one-stage approach, the IPD from all studies are analysed in a single step using an appropriate model that accounts for clustering of participants within studies and, potentially, between-study heterogeneity (e.g., a general or generalised linear mixed model). The best approach to take is debated in the literature, and so here we provide clearer guidance for a broad audience. Both approaches are important tools for IPDMA researchers and neither are a panacea. If most studies in the IPDMA are small (few participants or events), a one-stage approach is recommended due to using a more exact likelihood. However, in other situations, researchers can choose either approach, carefully following best practice. Some previous claims recommending to always use a one-stage approach are misleading, and the two-stage approach will often suffice for most researchers. When differences do arise between the two approaches, often it is caused by researchers using different modelling assumptions or estimation methods, rather than using one or two stages per se.

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两阶段还是不两阶段?这是IPD元分析项目的问题。
个体参与者数据荟萃分析(IPDMA)项目从多项研究中获取、检查、协调和综合原始数据。在进行荟萃分析时,研究人员必须在两阶段还是一阶段的方法之间做出决定。在两阶段方法中,首先在每个研究中分别分析IPD,以获得汇总数据(例如,治疗效果估计和标准误差);然后,在第二阶段,将这些汇总数据组合到标准的荟萃分析模型中(例如,共同效应或随机效应)。在一个阶段的方法中,使用适当的模型在一个步骤中分析所有研究的IPD,该模型考虑了研究中参与者的聚类,并可能考虑了研究之间的异质性(例如,通用或广义线性混合模型)。最佳方法在文献中有争议,因此我们在这里为广大观众提供了更清晰的指导。这两种方法都是IPDMA研究人员的重要工具,都不是灵丹妙药。如果IPDMA中的大多数研究规模较小(参与者或事件较少),则由于使用了更精确的可能性,建议采用一阶段方法。然而,在其他情况下,研究人员可以选择其中一种方法,仔细遵循最佳实践。以前一些建议始终使用一阶段方法的说法具有误导性,而两阶段方法通常足以满足大多数研究人员的需求。当这两种方法之间确实存在差异时,通常是由于研究人员使用了不同的建模假设或估计方法,而不是使用一两个阶段本身。
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来源期刊
Research Synthesis Methods
Research Synthesis Methods MATHEMATICAL & COMPUTATIONAL BIOLOGYMULTID-MULTIDISCIPLINARY SCIENCES
CiteScore
16.90
自引率
3.10%
发文量
75
期刊介绍: Research Synthesis Methods is a reputable, peer-reviewed journal that focuses on the development and dissemination of methods for conducting systematic research synthesis. Our aim is to advance the knowledge and application of research synthesis methods across various disciplines. Our journal provides a platform for the exchange of ideas and knowledge related to designing, conducting, analyzing, interpreting, reporting, and applying research synthesis. While research synthesis is commonly practiced in the health and social sciences, our journal also welcomes contributions from other fields to enrich the methodologies employed in research synthesis across scientific disciplines. By bridging different disciplines, we aim to foster collaboration and cross-fertilization of ideas, ultimately enhancing the quality and effectiveness of research synthesis methods. Whether you are a researcher, practitioner, or stakeholder involved in research synthesis, our journal strives to offer valuable insights and practical guidance for your work.
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