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Stat Med. 2007 Nov 10;26(25):4544-62.

Performance of the trim and fill method in the presence of publication bias and between-study heterogeneity.

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  • 1Department of Health Sciences, University of Leicester, Leicester LE1 7RH, U.K.

Abstract

The trim and fill method allows estimation of an adjusted meta-analysis estimate in the presence of publication bias. To date, the performance of the trim and fill method has had little assessment. In this paper, we provide a more comprehensive examination of different versions of the trim and fill method in a number of simulated meta-analysis scenarios, comparing results with those from usual unadjusted meta-analysis models and two simple alternatives, namely use of the estimate from: (i) the largest; or (ii) the most precise study in the meta-analysis. Findings suggest a great deal of variability in the performance of the different approaches. When there is large between-study heterogeneity the trim and fill method can underestimate the true positive effect when there is no publication bias. However, when publication bias is present the trim and fill method can give estimates that are less biased than the usual meta-analysis models. Although results suggest that the use of the estimate from the largest or most precise study seems a reasonable approach in the presence of publication bias, when between-study heterogeneity exists our simulations show that these estimates are quite biased. We conclude that in the presence of publication bias use of the trim and fill method can help to reduce the bias in pooled estimates, even though the performance of this method is not ideal. However, because we do not know whether funnel plot asymmetry is truly caused by publication bias, and because there is great variability in the performance of different trim and fill estimators and models in various meta-analysis scenarios, we recommend use of the trim and fill method as a form of sensitivity analysis as intended by the authors of the method.

Copyright 2007 John Wiley & Sons, Ltd.

PMID:
17476644
[PubMed - indexed for MEDLINE]
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