BMJ  2008;336:1413-1415 (21 June), doi:10.1136/bmj.a117

Analysis

Research methodology

Reasons or excuses for avoiding meta-analysis in forest plots

John P A Ioannidis, professor1, Nikolaos A Patsopoulos, research fellow1, Hannah R Rothstein, professor2

1 Clinical Trials and Evidence Based Medicine Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine and Biomedical Research Institute, Foundation for Research and Technology-Hellas, Ioannina 45110, Greece, 2 Department of Management, Zicklin School of Business, City University of New York, New York, NY 10010, USA

Correspondence to: J P A Ioannidis jioannid@cc.uoi.gr

Heterogeneous data are a common problem in meta-analysis. John Ioannidis, Nikolaos Patsopoulos, and Hannah Rothstein show that final synthesis is possible and desirable in most cases

The first 150 words of the full text of this article appear below.

Some systematic reviews simply assemble the eligible studies without performing meta-analysis. This may be a legitimate choice. However, an interesting situation arises when reviews present forest plots (quantitative effects and uncertainty per study) but do not calculate a summary estimate (the diamond at the bottom). These reviews imply that it is important to visualise the quantitative data but final synthesis is inappropriate. For example, a review of sexual abstinence programmes for HIV prevention claimed that owing to "data unavailability, lack of intention-to-treat analyses, and heterogeneity in programme and trial designs... a statistical meta-analysis would be inappropriate."1 As we discuss, options almost always exist for quantitative synthesis and sometimes they may offer useful insights. Reviewers and clinicians should be aware of these options, reflect carefully on their use, and understand their limitations.

Of the 1739 systematic reviews that included at least one forest plot with at least two studies in issue . . . [Full text of this article]


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