Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ

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• 作者：Danielle L. Burke ; Joie Ensor and Richard D. Riley
• 刊名：Statistics in Medicine
• 出版年：2017
• 出版时间：28 February 2017
• 年：2017
• 卷：36
• 期：5
• 页码：855-875
• 全文大小：492K
• ISSN：1097-0258

文摘

Meta-analysis using individual participant data (IPD) obtains and synthesises the raw, participant-level data from a set of relevant studies. The IPD approach is becoming an increasingly popular tool as an alternative to traditional aggregate data meta-analysis, especially as it avoids reliance on published results and provides an opportunity to investigate individual-level interactions, such as treatment-effect modifiers. There are two statistical approaches for conducting an IPD meta-analysis: one-stage and two-stage. The one-stage approach analyses the IPD from all studies simultaneously, for example, in a hierarchical regression model with random effects. The two-stage approach derives aggregate data (such as effect estimates) in each study separately and then combines these in a traditional meta-analysis model. There have been numerous comparisons of the one-stage and two-stage approaches via theoretical consideration, simulation and empirical examples, yet there remains confusion regarding when each approach should be adopted, and indeed why they may differ.