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BMJ No 7129 Volume 316

Letters Saturday 7 February 1998


Bias in meta-analysis detected by a simple, graphical test

Test had 10% false positive rate

Editor,
With examples of results from meta-analyses conflicting with those from subsequent large trials there is increasing need to distinguish the good from the not so good meta-analyses. To this end, Egger et al have developed a test for detecting bias in meta-analyses based on funnel plot asymmetry.(1) This test predicted discordance in meta-analyses. But, as with any significance test, there is also the possibility of falsely identifying bias when none existed. Since significance was defined by P0.1, the false positive rate of this test would be 10%. For instance, the quoted 13% (5/38) of the systematic reviews in the Cochrane Database showing bias may be attributed to chance alone.

Defining significance to be P0.1 enabled the test to predict discordant meta-analyses - the conventional P0.05 produced significant bias in only one of the four discordant meta-analyses - but resulted in a 10% false positive rate. Some may consider this rate of false positive results to be unacceptably high. Be that as it may, these findings showed the continuing need for care in the interpretation of results of significance tests. These comments, however, should not detract from the importance of looking for bias in meta-analyses and the potential benefits this test may bring to screening for such bias.

Valerie Seagroatt University research lecturer
Unit of Healthcare Epidemiology,
Institute of Health Sciences,
Oxford University,
Oxford OX3 7LF

Irene Stratton University research lecturer
Diabetes Research Laboratories,
Radcliffe Infirmary,
Oxford OX2 6HE

References

1 Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997;315:629-34. (13 September.)


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