Integrated Likelihood Inference in Small Sample Meta-analysis for Continuous Outcomes

This paper proposes the use of the integrated likelihood for inference on the mean effect in small-sample meta-analysis for continuous outcomes. The method eliminates the nuisance parameters given by variance components through integration with respect to a suitable weight function, with no need to estimate them. The integrated likelihood approach takes into proper account the estimation uncertainty of within-study variances, thus providing confidence intervals with empirical coverage closer to nominal levels than standard likelihood methods. The improve- ment is remarkable when either (i) the number of studies is small to moderate or (ii) the small sample size of the studies does not allow to consider the within-study variances as known, as common in applications. Moreover, the use of the integrated likelihood avoids numerical pitfalls related to the estimation of variance components which can affect alternative likelihood approaches. The proposed methodology is illustrated via simulation and applied to a meta-analysis study in nutritional science.