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Neither fixed nor random: weighted least squares meta-analysis

journal contribution
posted on 2015-06-15, 00:00 authored by Tom StanleyTom Stanley, Chris DoucouliagosChris Doucouliagos
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.

History

Journal

Statistics in medicine

Volume

34

Issue

13

Pagination

2116 - 2127

Publisher

Wiley-Blackwell

Location

Chichester, Eng.

ISSN

0277-6715

Language

eng

Publication classification

C Journal article; C1.1 Refereed article in a scholarly journal

Copyright notice

2015, John Wiley & Sons