Systematic Evaluation and Comparison of Statistical Tests for Publication Bias

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BACKGROUND: This study evaluates the statistical and discriminatory powers of three statistical test methods (Beggs, Eggers, and Macaskills) to detect publication bias in meta-analyses.METHODS: The data sources were 130 reviews from the Cochrane Database of Systematic Reviews 2002 issue, which considered a binary endpoint and contained 10 or more individual studies. Funnel plots with observersagreements were selected as a reference standard. We evaluated a trade-off between sensitivity and specificity by varying cut-off p-values, power of statistical tests given fixed false positive rates, and area under the receiver operating characteristic curve.RESULTS: In 36 reviews, 733 original studies evaluated 2,874,006 subjects. The number of trials included in each ranged from 10 to 70 (median 14.5). Given that the false positive rate was 0.1, the sensitivity of Eggers method was 0.93, and was larger than that of Beggs method (0.86) and Macaskills method (0.43). The sensitivities of three statistical tests increased as the cut-off p-values increased without a substantial decrement of specificities. The area under the ROC curve of Eggers method was 0.955 (95% confidence interval, 0.889-1.000) and was not different from that of Beggs method (area=0.913, p=0.2302), but it was larger than that of Macaskills method (area=0.719, p=0.0116).CONCLUSION: Eggers linear regression method and Beggs method had stronger statistical and discriminatory powers than Macaskills method for detecting publication bias given the same type I error level. The power of these methods could be improved by increasing the cut-off p-value without a substantial increment of false positive rate.J Epidemiol 2005; 15: 235-243.
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Systematic Evaluation and Comparison of Statistical Tests for Publication Bias

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