Rovers Maroeska M, Reitsma Johannes B
UMC St Radboud, afd. Operatiekamers en afd. Epidemiologie, Biostatistiek & HTA, Nijmegen, the Netherlands.
Ned Tijdschr Geneeskd. 2012;156(31):A4743.
An IPD (Individual Participant Data) meta-analysis requires collecting original individual patient data and calculating an estimated effect based on these data. The use of individual patient data has various advantages: the original data and the results of published analyses are verified, comparability between studies in terms of definitions, coding and analyses is increased, the number of options for performing sub-group analyses becomes greater, and it becomes possible to conduct more complex statistical analyses, such as the pooling of time-dependent data and multivariate regression analyses. In an IPD meta-analysis, additional information can be used which was not mentioned in the original article, for example, data from long-term follow-up. Improvements to this methodology are still possible; for example, to find the right balance between sufficient power to detect clinically relevant subgroup effects and minimizing the risk of false-positive findings. Readers can evaluate an IPD meta-analysis on various factors, including the reason for the choice for an IPD meta-analysis, the method used to identify and select the studies, and the number of approached authors that made their data available.
个体参与者数据（IPD）荟萃分析需要收集原始个体患者数据，并基于这些数据计算估计效应。使用个体患者数据有诸多优势：原始数据和已发表分析结果得到验证，研究之间在定义、编码和分析方面的可比性增强，进行亚组分析的选项增多，并且能够开展更复杂的统计分析，如时间依赖性数据的合并和多元回归分析。在IPD荟萃分析中，可以使用原始文章中未提及的额外信息，例如长期随访数据。这种方法仍有改进空间；例如，要在检测临床相关亚组效应的足够效能与将假阳性结果风险降至最低之间找到恰当平衡。读者可以根据各种因素评估IPD荟萃分析，包括选择IPD荟萃分析的原因、用于识别和选择研究的方法，以及提供数据的被联系作者数量。
