Meta-regression approximations to reduce publication selection bias

• Published in: Research synthesis methods Vol. 5; no. 1; pp. 60 - 78
• Main Authors: Stanley, T. D., Doucouliagos, Hristos
• Format: Journal Article
• Language: English
• Published: Chichester Blackwell Publishing Ltd 01.03.2014; Wiley-Blackwell; Wiley; Wiley Subscription Services, Inc
• Subjects: Algebra; Applied sciences; Approximation; Bias; Clinical Trials as Topic - classification; Clinical Trials as Topic - statistics & numerical data; Computer science; control theory; systems; Computer Simulation; Data Interpretation, Statistical; Error of Measurement; Evidence-Based Medicine; Exact sciences and technology; Information systems. Data bases; Linear inference, regression; Mathematical Formulas; Mathematics; Memory organisation. Data processing; Meta Analysis; Meta-Analysis as Topic; meta-regression; Models, Statistical; Monte Carlo Methods; Polynomials; Predictive Value of Tests; Probability and statistics; Publication Bias - statistics & numerical data; publication selection bias; Regression (Statistics); Regression Analysis; Sciences and techniques of general use; Software; Statistical Bias; Statistics; systematic reviews; systematic reviews, truncation; truncation; Policy; Therapy; Polynomial approximation; Bias; publication selection bias; Systematic review; Modeling; Mean square error; Metaanalysis; meta-regression; Heterogeneity; Model matching; Quadratic approximation; Metamodel; truncation; Monte Carlo method; Wage; Statistical analysis; Empirical method; Replacement; Regression analysis; Standards; Truncated shape; systematic reviews; Integrity; systematic reviews, truncation

Publication 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 l...