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Open Access Research article

Assessment of regression-based methods to adjust for publication bias through a comprehensive simulation study

Santiago G Moreno*, Alex J Sutton, AE Ades, Tom D Stanley, Keith R Abrams, Jaime L Peters and Nicola J Cooper

BMC Medical Research Methodology 2009, 9:2  doi:10.1186/1471-2288-9-2

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regression method of "Peter"

Rainer Beier   (2011-03-15 10:56)  Federal institute for drugs and Medical advices (BfArM) Germany email

It is always a very strong problem for analysing meta-analysis correctly additionally for heterogeneity. In my oppinion the regression model of Peter is the best way to detect publication bias for binary data because it depends on the sample size of the studies, where the sample size is weighted by a function of sample size as described in this article. All other regression models for detecting publication bias do not consider the problem of sample size. But it is very important for investigating meta analysis to consider the sample sizes of the studies included in a meta analysis where small studies with a greater effect could be included compared with larger studies included with a lower effect. Additionally these detecting models do not work if high heterogeneity is given, but in this case you are not able to interpret meta-analysis (ICH E9). In literature these methods of detecting publications bias were often critized in a negative way but it is a way to present better
scientific results.

Competing interests

There are no conflicts of interests.This is only the opinion of the author and not from BfArM generally

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