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The devil is in the details…or not? A primer on individual patient data meta-analysis
  1. Sachin Sud, MD1,
  2. James Douketis, MD2
  1. 1University of Toronto, Toronto, Ontario, Canada
  2. 2McMaster University, Hamilton, Ontario, Canada

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A systematic review is the process by which primary studies are identified, critically appraised, and interpreted according to a predefined plan to answer clinically important questions with minimal bias and random error. When the results are quantitatively combined, the review is referred to as a “meta-analysis.” Meta-analysis provides more precise estimates of treatment benefits and harms, may reveal treatment effects that would otherwise go undetected in individual trials, and provides a succinct “bottom line” for a clinical question based on the best available evidence.1 A variation of this method is individual patient data (IPD) meta-analysis where analyses are done using original data and outcomes for each person enrolled in relevant studies; patient databases from each study are combined into a single large database, and analysed using methods that account for variation both within studies and between studies.

What is the difference between conventional and IPD meta-analysis? In conventional meta-analysis, aggregated data are extracted from published and unpublished reports according to a predetermined protocol. Analysis is performed by calculating a weighted average for effect (eg, relative risk) across randomised trials.2 Limitations of this approach include risk of publication bias,3 heterogeneity in trial results,4 inability to perform intention-to-treat analyses when relevant patient data are excluded or missing,5 and limited methodological quality of source studies.6 Because each randomised trial in a …

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