Prevention, Assessment and Adjustments, The Atrium, Southern Gate, Chichester: John Wiley & Sons, Ltd. Borenstein (Hrsg.), Publication Bias in Meta-Analysis. Minder (1997), Bias in meta-analysis detected by a simple, graphical test British Medical Journal 315: 629-634. For large studies, however, we will observe large SNDs and the inverse standard errors will also be large (Egger et al 1997: 629f.).Įgger, M., G. Again, for small studies, the SND will be close to zero.
(2) Even though small studies may produce large effect sizes, the SND will be small since the standard error will be large. (1) Since the standard error depends on sample size, the inverse standard error for small studies will be close to zero. In other words, the intercepts provide a measure of funnel plot asymmetry (Sterne/Egger 2005: 101). We improve the precision of an estimate by making use of all available data. Meta-analysis takes data from several different studies and produces a single estimate of the effect, usually of a treatment or risk factor. When there is no evidence of funnel plot asymmetry, the intercept should not significantly differ from zero, i.e. reviews include a meta-analysis, but not all.
Then, an unweighted OLS regression is estimated. The inverse standard error (“precision”) serves as predictor variable. Most of these regression approaches are using the so-called standard normal deviate (SND) which is defined as effect size divided by its standard error ($ES_i / SE_i$). The following can give you an idea of the underlying logic of applying this regression model to test for publication bias: Has already given an answer, i.e there is evidence of funnel plot I would be interested in the reference that suggests using a one-tailed test.
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