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Better than random: weighted least squares meta-regression analysis

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posted on 2013-01-01, 00:00 authored by T D Stanley, Chris DoucouliagosChris Doucouliagos
Our study revisits and challenges two core conventional meta-regression models: the prevalent use of ?mixed-effects? or random-effects meta-regression analysis (RE-MRA) and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why the traditional, unrestricted weighted least squares estimator (WLS-MRA) is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias and as good as FE-MRA in all cases and better in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients with confidence intervals that are comparable to mixed-or random-effects when there is no publication bias. When there is publication selection bias, WLS-MRA dominates mixed- and random-effects, especially when there is large additive heterogeneity as assumed by the random-effects meta-regression model.

History

Series

School Working Paper - Economics Series ; SWP 2013/2

Pagination

1 - 26

Publisher

Deakin University, School of Accounting, Economics and Finance

Place of publication

Geelong, Vic.

Language

eng

Notes

School working paper (Deakin University. School of Accounting, Economics and Finance) ; 2013/2 Our study revisits and challenges two core conventional meta-regression models: the prevalent use of ?mixed-effects? or random-effects meta-regression analysis (RE-MRA) and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why the traditional, unrestricted weighted least squares estimator (WLS-MRA) is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias and as good as FE-MRA in all cases and better in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients with confidence intervals that are comparable to mixed-or random-effects when there is no publication bias. When there is publication selection bias, WLS-MRA dominates mixed- and random-effects, especially when there is large additive heterogeneity as assumed by the random-effects meta-regression model.

Publication classification

CN.1 Other journal article

Copyright notice

2013, The Authors

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