Neither fixed nor random: weighted least squares meta-analysis

Stat Med. 2015 Jun 15;34(13):2116-27. doi: 10.1002/sim.6481. Epub 2015 Mar 23.

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.

Keywords: fixed effect; meta-analysis; meta-regression; random effects; weighted least squares.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bias*
  • Computer Simulation
  • Confidence Intervals
  • Humans
  • Least-Squares Analysis*
  • Markov Chains
  • Meta-Analysis as Topic*
  • Publication Bias*