when heterogeneity is present. This motivated the development of a random-effects method based on a null model of no-heterogeneity, which increases power over traditional random-effects methods5. Under this framework, a statistical test against a null model of no-heterogeneity can be viewed as a summation of a fixed-effect component and a heterogeneity component, thus connecting fixed-effects and random-effects meta-analysis5. Subsequent work has introduced the concept of posterior probability that each study has an effect, aiding interpretation and power under the assumption that a subset of studies may have a negligible effect on the trait6.
