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. Author manuscript; available in PMC: 2021 Feb 1.
Published in final edited form as: BMJ Evid Based Med. 2019 Jul 4;25(1):27–32. doi: 10.1136/bmjebm-2019-111191

Table 1.

Statistical methods for assessing small-study effects.

Method Description
Funnel plot Presenting the study-specific effect size against its standard error (or the inverse of standard error). It is roughly symmetrical around the overall effect size if no small-study effects appear.
Regression test SND = α + β× precision + error. Under the fixed-effect setting, SND (standard normal deviate) = y/s and precision =1/s; under the random-effects setting, SND = y/s2+τ2 and precision =1/s2+τ2. Here, y and s are the study-specific effect size and its standard error within studies, respectively, and τ2 is between-study variance due to heterogeneity. It tests for whether α = 0.
Regression intercept (TIFE or TIRE) An estimate of the intercept α of the regression test under the fixed-effect (TIFE) or random-effects (TIRE) setting.
Skewness (TSFE or TSRE) An estimate of the skewness of the study-specific errors of the regression test under the fixed-effect (TSFE) or random-effects (TSRE) setting.
Trim-and-fill method Estimating the suppressed studies and thus correcting small-study effects based on funnel plot’s asymmetry.
Proportion of suppressed studies (PTF) PTF=k^0/(n+k^0)×100%, where n is the number of studies in the original meta-analysis, and k^0 is the estimated number of suppressed studies using the trim-and-fill method.
Relative change of overall result by incorporating imputed suppressed studies (RTF) RTF=(θ^published data/θ^total data1)×100%, where θ^published data is the estimated overall result in the original meta-analysis of published studies, and θ^total data is that after incorporating imputed suppressed studies using the trim-and-fill method.