A random-effects regression model for meta-analysis.
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.
Berkey
C S
CS
Technology Assessment Group, Harvard School of Public Health, Boston, MA 02115, USA.
Hoaglin
D C
DC
Mosteller
F
F
Colditz
G A
GA
D001500:BCG Vaccine / Q000008:administration & dosage; D015982:Bias; D002986:Clinical Trials as Topic; D016001:Confidence Intervals; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D011336:Probability; D012044:Regression Analysis; D014376:Tuberculosis / Q000517:prevention & control
D001500:BCG Vaccine