Subgroup effects despite homogeneous heterogeneity test results.
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| Abstract | BACKGROUND: Statistical tests of heterogeneity are very popular in meta-analyses, as heterogeneity might indicate subgroup effects. Lack of demonstrable statistical heterogeneity, however, might obscure clinical heterogeneity, meaning clinically relevant subgroup effects. METHODS: A qualitative, visual method to explore the potential for subgroup effects was provided by a modification of the forest plot, i.e., adding a vertical axis indicating the proportion of a subgroup variable in the individual trials. Such a plot was used to assess the potential for clinically relevant subgroup effects and was illustrated by a clinical example on the effects of antibiotics in children with acute otitis media. RESULTS: Statistical tests did not indicate heterogeneity in the meta-analysis on the effects of amoxicillin on acute otitis media (Q = 3.29, p = 0.51; I2 = 0%; T2 = 0). Nevertheless, in a modified forest plot, in which the individual trials were ordered by the proportion of children with bilateral otitis, a clear relation between bilaterality and treatment effects was observed (which was also found in an individual patient data meta-analysis of the included trials: p-value for interaction 0.021). CONCLUSIONS: A modification of the forest plot, by including an additional (vertical) axis indicating the proportion of a certain subgroup variable, is a qualitative, visual, and easy-to-interpret method to explore potential subgroup effects in studies included in meta-analyses.
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| Authors | Rolf H H Groenwold, Maroeska M Rovers, Jacobus Lubsen, Geert Jmg van der Heijden |
| Journal | BMC medical research methodology (BMC Med Res Methodol) Vol. 10 Pg. 43 (May 17 2010) ISSN: 1471-2288 [Electronic] England |
| PMID | 20478021 (Publication Type: Journal Article) |
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