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...

Our study revisits and challenges two core conventional meta-regression models: the prevalent use of ‘mixed-effects’ or random-effects meta-regression analysis (RE-MRA) and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain...