Luis Furuya-Kanamori1, Chang Xu2, Lifeng Lin3, Tinh Doan3, Haitao Chu4, Lukman Thalib5, Suhail A R Doi2. 1. Research School of Population Health, ANU College of Health and Medicine, Australian National University, Australian Capital Territory, Australia. Electronic address: luis.furuya-kanamori@anu.edu.au. 2. Department of Population Medicine, College of Medicine, Qatar University, Doha, Qatar. 3. Department of Statistics, Florida State University, Tallahassee, FL, USA. 4. Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA. 5. Department of Public Health, College of Health Sciences, Qatar University, Doha, Qatar.
Abstract
OBJECTIVES: The aim of the study was to investigate the effect of number of studies in a meta-analysis on the detection of publication bias using P value-driven methods. METHODS: The proportion of meta-analyses detected by Egger's, Harbord's, Peters', and Begg's tests to have asymmetry suggestive of publication bias were examined in 5,014 meta-analyses from Cochrane reviews. P values were also assessed in meta-analyses with varying number of studies, whereas symmetry was held constant. A simulation study was conducted to investigate if the above tests underestimate or overestimate the presence of publication bias. RESULTS: The proportion of meta-analyses detected as asymmetrical via Egger's, Harbord's, Peters', and Begg's tests decreased by 42.6%, 41.1%, 29.3%, and 28.3%, respectively, when the median number of studies in the meta-analysis decreased from 87 to 14. P values decreased as the number of studies increased in the meta-analysis, despite the level of symmetry remaining constant. The simulation study confirmed that when publication bias is present, P value tests underestimate the presence of publication bias, particularly when study numbers are small. CONCLUSION: P value-based tests used for the detection of publication bias-related asymmetry in meta-analysis require careful examination, as they underestimate asymmetry. Alternative methods not dependent on the number of studies are preferable.
OBJECTIVES: The aim of the study was to investigate the effect of number of studies in a meta-analysis on the detection of publication bias using P value-driven methods. METHODS: The proportion of meta-analyses detected by Egger's, Harbord's, Peters', and Begg's tests to have asymmetry suggestive of publication bias were examined in 5,014 meta-analyses from Cochrane reviews. P values were also assessed in meta-analyses with varying number of studies, whereas symmetry was held constant. A simulation study was conducted to investigate if the above tests underestimate or overestimate the presence of publication bias. RESULTS: The proportion of meta-analyses detected as asymmetrical via Egger's, Harbord's, Peters', and Begg's tests decreased by 42.6%, 41.1%, 29.3%, and 28.3%, respectively, when the median number of studies in the meta-analysis decreased from 87 to 14. P values decreased as the number of studies increased in the meta-analysis, despite the level of symmetry remaining constant. The simulation study confirmed that when publication bias is present, P value tests underestimate the presence of publication bias, particularly when study numbers are small. CONCLUSION: P value-based tests used for the detection of publication bias-related asymmetry in meta-analysis require careful examination, as they underestimate asymmetry. Alternative methods not dependent on the number of studies are preferable.
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