Suppr超能文献

个体参与者数据荟萃分析的统计分析:方法比较与实践建议。

Statistical analysis of individual participant data meta-analyses: a comparison of methods and recommendations for practice.

机构信息

Centre for Reviews and Dissemination, University of York, York, United Kingdom.

出版信息

PLoS One. 2012;7(10):e46042. doi: 10.1371/journal.pone.0046042. Epub 2012 Oct 3.

Abstract

BACKGROUND

Individual participant data (IPD) meta-analyses that obtain "raw" data from studies rather than summary data typically adopt a "two-stage" approach to analysis whereby IPD within trials generate summary measures, which are combined using standard meta-analytical methods. Recently, a range of "one-stage" approaches which combine all individual participant data in a single meta-analysis have been suggested as providing a more powerful and flexible approach. However, they are more complex to implement and require statistical support. This study uses a dataset to compare "two-stage" and "one-stage" models of varying complexity, to ascertain whether results obtained from the approaches differ in a clinically meaningful way.

METHODS AND FINDINGS

We included data from 24 randomised controlled trials, evaluating antiplatelet agents, for the prevention of pre-eclampsia in pregnancy. We performed two-stage and one-stage IPD meta-analyses to estimate overall treatment effect and to explore potential treatment interactions whereby particular types of women and their babies might benefit differentially from receiving antiplatelets. Two-stage and one-stage approaches gave similar results, showing a benefit of using anti-platelets (Relative risk 0.90, 95% CI 0.84 to 0.97). Neither approach suggested that any particular type of women benefited more or less from antiplatelets. There were no material differences in results between different types of one-stage model.

CONCLUSIONS

For these data, two-stage and one-stage approaches to analysis produce similar results. Although one-stage models offer a flexible environment for exploring model structure and are useful where across study patterns relating to types of participant, intervention and outcome mask similar relationships within trials, the additional insights provided by their usage may not outweigh the costs of statistical support for routine application in syntheses of randomised controlled trials. Researchers considering undertaking an IPD meta-analysis should not necessarily be deterred by a perceived need for sophisticated statistical methods when combining information from large randomised trials.

摘要

背景

个体参与者数据(IPD)荟萃分析从研究中获取“原始”数据,而不是汇总数据,通常采用“两阶段”方法进行分析,即试验内的 IPD 生成汇总指标,然后使用标准荟萃分析方法进行合并。最近,提出了一系列“一阶段”方法,这些方法将所有个体参与者数据合并在单个荟萃分析中,被认为是一种更强大、更灵活的方法。然而,它们的实施更为复杂,需要统计学支持。本研究使用数据集比较了不同复杂程度的“两阶段”和“一阶段”模型,以确定从这些方法中获得的结果是否在临床上有意义的方式上有所不同。

方法和发现

我们纳入了 24 项随机对照试验的数据,评估了抗血小板药物在预防妊娠子痫前期中的作用。我们进行了两阶段和一阶段 IPD 荟萃分析,以估计总体治疗效果,并探索潜在的治疗相互作用,即特定类型的妇女和她们的婴儿可能从接受抗血小板治疗中获得不同程度的益处。两阶段和一阶段方法得出的结果相似,表明使用抗血小板药物有益(相对风险 0.90,95%CI 0.84 至 0.97)。两种方法都没有表明任何特定类型的妇女从抗血小板治疗中获益更多或更少。不同类型的一阶段模型之间的结果没有明显差异。

结论

对于这些数据,两阶段和一阶段分析方法得出的结果相似。尽管一阶段模型为探索模型结构提供了一个灵活的环境,并且在与参与者类型、干预措施和结局相关的研究模式掩盖了试验内相似关系的情况下很有用,但它们使用提供的额外见解可能不会超过在随机对照试验综合分析中应用统计支持的成本。研究人员在考虑进行 IPD 荟萃分析时,不应该因为需要复杂的统计方法而望而却步,特别是在将大型随机试验的信息进行合并时。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc83/3463584/f9cc78ee2194/pone.0046042.g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验