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Meta-regression approximations to reduce publication selection bias
journal contribution
posted on 2014-03-01, 00:00 authored by Tom StanleyTom Stanley, Chris DoucouliagosChris DoucouliagosPublication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with standard error (PEESE), is shown to have the smallest bias and mean squared error in most cases and to outperform conventional meta-analysis estimators, often by a great deal. Monte Carlo simulations also demonstrate how a new hybrid estimator that conditionally combines PEESE and the Egger regression intercept can provide a practical solution to publication selection bias. PEESE is easily expanded to accommodate systematic heterogeneity along with complex and differential publication selection bias that is related to moderator variables. By providing an intuitive reason for these approximations, we can also explain why the Egger regression works so well and when it does not. These meta-regression methods are applied to several policy-relevant areas of research including antidepressant effectiveness, the value of a statistical life, the minimum wage, and nicotine replacement therapy.
History
Journal
Research synthetis methodsVolume
5Issue
1Pagination
60 - 78Publisher
Wiley-BlackwellLocation
London, Eng.Publisher DOI
eISSN
1759-2887Language
engPublication classification
C1.1 Refereed article in a scholarly journal; C Journal articleCopyright notice
2013, John Wiley & SonsUsage metrics
Keywords
meta-regressionpublication selection biassystematic reviews, truncationClinical Trials as TopicComputer SimulationData Interpretation, StatisticalEvidence-Based MedicineMeta-Analysis as TopicModels, StatisticalPredictive Value of TestsPublication BiasRegression AnalysisScience & TechnologyLife Sciences & BiomedicineMathematical & Computational BiologyMultidisciplinary SciencesScience & Technology - Other Topicssystematic reviewstruncationTRIALSLIFEStatisticsEconomic Theory not elsewhere classified
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