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Neither fixed nor random: weighted least squares meta-analysis
journal contribution
posted on 2015-06-15, 00:00 authored by Tom StanleyTom Stanley, Chris DoucouliagosChris DoucouliagosThis study challenges two core conventional meta-analysis methods: fixed effect and random effects. We show how and explain why an unrestricted weighted least squares estimator is superior to conventional random-effects meta-analysis when there is publication (or small-sample) bias and better than a fixed-effect weighted average if there is heterogeneity. Statistical theory and simulations of effect sizes, log odds ratios and regression coefficients demonstrate that this unrestricted weighted least squares estimator provides satisfactory estimates and confidence intervals that are comparable to random effects when there is no publication (or small-sample) bias and identical to fixed-effect meta-analysis when there is no heterogeneity. When there is publication selection bias, the unrestricted weighted least squares approach dominates random effects; when there is excess heterogeneity, it is clearly superior to fixed-effect meta-analysis. In practical applications, an unrestricted weighted least squares weighted average will often provide superior estimates to both conventional fixed and random effects.
History
Journal
Statistics in medicineVolume
34Issue
13Pagination
2116 - 2127Publisher
Wiley-BlackwellLocation
Chichester, Eng.Publisher DOI
ISSN
0277-6715Language
engPublication classification
C Journal article; C1.1 Refereed article in a scholarly journalCopyright notice
2015, John Wiley & SonsUsage metrics
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Keywords
meta-analysismeta-regressionweighted least squaresfixed effectrandom effectsScience & TechnologyLife Sciences & BiomedicinePhysical SciencesMathematical & Computational BiologyPublic, Environmental & Occupational HealthMedical InformaticsMedicine, Research & ExperimentalStatistics & ProbabilityResearch & Experimental MedicineMathematicsHETEROGENEITYPUBLICATIONAPPROXIMATIONSBIASmeta-regression analysisStatistics
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