Tests of small-study effects have been popular since the mid-1990s. They evaluate studies included in the same meta-analysis and assess whether effect sizes are related to study size. When small studies have larger effects than large studies, this may reflect publication or selective reporting biases, but alternative explanations exist (as reviewed elsewhere [10]). Sensitivity and specificity of these tests in real life is unknown, but simulation studies have evaluated the performance in different settings. Published recommendations suggest cautious use of such tests [10]. In brief, visual evaluations of inverted funnel plots without statistical testing are precarious [11]; some test variants have better type I and type II error properties than others [12, 13]; and for most meta-analyses where there are a limited number of studies, the power of these tests is low [10].
