Neither fixed nor random: weighted least squares meta-analysis
This 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...
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| Published in | Statistics in medicine Vol. 34; no. 13; pp. 2116 - 2127 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
Blackwell Publishing Ltd
15.06.2015
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This 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. Copyright © 2015 John Wiley & Sons, Ltd. |
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| AbstractList | This 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. This 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. Copyright © 2015 John Wiley & Sons, Ltd. |
| Author | Doucouliagos, Hristos Stanley, T. D. |
| Author_xml | – sequence: 1 givenname: T. D. surname: Stanley fullname: Stanley, T. D. email: Correspondence to: T. D. Stanley, Julia Mobley Professor of Economics, Hendrix College, 1600 Washington St., Conway, AR, 72032, U.S.A., Stanley@hendrix.edu organization: Department of Economics, Hendrix College, 1600 Washington St., AR, 72032, Conway, U.S.A – sequence: 2 givenname: Hristos surname: Doucouliagos fullname: Doucouliagos, Hristos organization: Department of Economics, Deakin University, 221 Burwood Highway, Victoria, 3125, Burwood, Australia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25809462$$D View this record in MEDLINE/PubMed |
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| Keywords | random effects fixed effect meta-analysis weighted least squares meta-regression |
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| Title | Neither fixed nor random: weighted least squares meta-analysis |
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