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Using network meta‐analysis to evaluate the existence of small‐study effects in a network of interventions[Note 2. A, aspirin; D + A, dipyridamole + aspirin; P, placebo. ...]
Author(s) -
Chaimani Anna,
Salanti Georgia
Publication year - 2012
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.57
Subject(s) - funnel plot , meta analysis , publication bias , meta regression , psychological intervention , econometrics , regression analysis , computer science , regression , field (mathematics) , statistics , psychology , machine learning , mathematics , medicine , psychiatry , pure mathematics
Suggested methods for exploring the presence of small‐study effects in a meta‐analysis and the possibility of publication bias are associated with important limitations. When a meta‐analysis comprises only a few studies, funnel plots are difficult to interpret, and regression‐based approaches to test and account for small‐study effects have low power. Assuming that the cause of funnel plot asymmetry is likely to affect an entire research field rather than only a particular comparison of interventions, we suggest that network meta‐regression is employed to account for small‐study effects in a set of related meta‐analyses. We present several possible models for the direction and distribution of small‐study effects and we describe the methods by re‐analysing two published networks. Copyright © 2012 John Wiley & Sons, Ltd.