A random-effects regression model for meta-analysis

Stat Med. 1995 Feb 28;14(4):395-411. doi: 10.1002/sim.4780140406.


Many meta-analyses use a random-effects model to account for heterogeneity among study results, beyond the variation associated with fixed effects. A random-effects regression approach for the synthesis of 2 x 2 tables allows the inclusion of covariates that may explain heterogeneity. A simulation study found that the random-effects regression method performs well in the context of a meta-analysis of the efficacy of a vaccine for the prevention of tuberculosis, where certain factors are thought to modify vaccine efficacy. A smoothed estimator of the within-study variances produced less bias in the estimated regression coefficients. The method provided very good power for detecting a non-zero intercept term (representing overall treatment efficacy) but low power for detecting a weak covariate in a meta-analysis of 10 studies. We illustrate the model by exploring the relationship between vaccine efficacy and one factor thought to modify efficacy. The model also applies to the meta-analysis of continuous outcomes when covariates are present.

Publication types

  • Comparative Study
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • BCG Vaccine / administration & dosage
  • Bias
  • Clinical Trials as Topic
  • Confidence Intervals
  • Humans
  • Meta-Analysis as Topic*
  • Models, Statistical*
  • Probability
  • Regression Analysis*
  • Tuberculosis / prevention & control


  • BCG Vaccine