Bench-to-bedside review: Avoiding pitfalls in critical care meta-analysis – funnel plots, risk estimates, types of heterogeneity, baseline risk and the ecologic fallacy

doi:10.1186/cc6941

Critical Care
Volume 12
Issue 4

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This article is part of a series on Translational Research, edited by Dr John Kellum.

Review

Bench-to-bedside review: Avoiding pitfalls in critical care meta-analysis – funnel plots, risk estimates, types of heterogeneity, baseline risk and the ecologic fallacy

Michael C Reade¹^,2 , Anthony Delaney³ , Michael J Bailey⁴ and Derek C Angus¹

¹CRISMA Laboratory, Department of Critical Care Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA, 15261, USA

²Austin Hospital, University of Melbourne, 145 Studley Road, Heidelberg 3084, Australia

³Royal North Shore Hospital and Northern Clinical School, University of Sydney, Pacific Highway, St Leonards, NSW, 2065, Australia

⁴Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, The Alfred Hospital, Commercial Road, Melbourne 3004, Australia

author email corresponding author email

Critical Care 2008, 12:220doi:10.1186/cc6941


Published:	25 July 2008

Abstract

Meta-analysis can be a powerful tool for demonstrating the applicability of a concept beyond the context of individual clinical trials and observational studies, including exploration of effects across different subgroups. Meta-analysis avoids Simpson's paradox, in which a consistent effect in constituent trials is reversed when results are simply pooled. Meta-analysis in critical care medicine is made more complicated, however, by the heterogeneous nature of critically ill patients and the contexts within which they are treated. Failure to properly adjust for this heterogeneity risks missing important subgroup effects in, for example, the interaction of treatment with varying levels of baseline risk. When subgroups are defined by characteristics that vary within constituent trials (such as age) rather than features constant within each trial (such as drug dose), there is the additional risk of incorrect conclusions due to the ecological fallacy. The present review explains these problems and the strategies by which they are overcome.