Abstract
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.In this tutorial paper, we outline the key statistical methods for one‐stage and two‐stage IPD meta‐analyses, and provide 10 key reasons why they may produce different summary results. We explain that most differences arise because of different modelling assumptions, rather than the choice of one‐stage or two‐stage itself. We illustrate the concepts with recently published IPD meta‐analyses, summarise key statistical software and provide recommendations for future IPD meta‐analyses. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Highlights
Statistical methods for meta-analysis and evidence synthesis are increasingly popular tools in medical research, as they synthesise quantitative information across multiple studies to produce evidence-based results
To aid researchers facing this situation, we describe 10 key reasons why such differences may arise even when the same individual participant data (IPD) are used for both one-stage and two-stage approaches
We illustrate this issue with a previous IPD meta-analysis dataset of three trials that investigated whether erythema is a risk factor for deep vein thrombosis (DVT), as shown in Debray et al [3]
Summary
Statistical methods for meta-analysis and evidence synthesis are increasingly popular tools in medical research, as they synthesise quantitative information across multiple studies to produce evidence-based results. An important advantage is being able to model individual-level interactions directly within studies, which has substantially greater power and avoids ecological bias compared with a meta-regression of aggregate data across studies [4,5] For such reasons, there has been an increase in the number of IPD meta-analyses in the last decade [5,6]. Debray et al found that erythema was a statistically significant predictor of deep vein thrombosis (DVT) in a one-stage analysis (p = 0.03), but not a two-stage analysis (p = 0.12) [3] For this reason, Tierney et al advise, ‘It is important, that the choice of one or two-stage analysis is specified in advance or that results for both approaches are reported’ [13].
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