Meta-regression approximations to reduce publication selection bias
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...
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Published in | Research synthesis methods Vol. 5; no. 1; pp. 60 - 78 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Chichester
Blackwell Publishing Ltd
01.03.2014
Wiley-Blackwell Wiley Wiley Subscription Services, Inc |
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Online Access | Get full text |
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Abstract | 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 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. Copyright © 2013 John Wiley & Sons, Ltd. |
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AbstractList | 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 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. 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 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. Copyright © 2013 John Wiley & Sons, Ltd. [PUBLICATION ABSTRACT] 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 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. Copyright © 2013 John Wiley & Sons, Ltd. |
Author | Doucouliagos, Hristos Stanley, T. D. |
Author_xml | – sequence: 1 givenname: T. D. surname: Stanley fullname: Stanley, T. D. email: Stanley@hendrix.edu organization: Economics, Hendrix College, 1600 Washington St., AR, 72032, Conway, USA – sequence: 2 givenname: Hristos surname: Doucouliagos fullname: Doucouliagos, Hristos organization: School of Accounting, Economics, and Finance and Alfred Deakin Research Institute, Deakin University, 221 Burwood Highway, Victoria, 3125, Burwood, Australia |
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Keywords | 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 |
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SubjectTerms | 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 |
Title | Meta-regression approximations to reduce publication selection bias |
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