This is an archived version of the Handbook. For the current version, please go to or search for this chapter here.  Random-effects (DerSimonian and Laird) method for meta-analysis

A variation on the inverse-variance method is to incorporate an assumption that the different studies are estimating different, yet related, intervention effects. This produces a random-effects meta-analysis, and the simplest version is known as the DerSimonian and Laird method (DerSimonian 1986). Random-effects meta-analysis is discussed in Section 9.5.4. To undertake a random-effects meta-analysis, the standard errors of the study-specific estimates (SEi above) are adjusted to incorporate a measure of the extent of variation, or heterogeneity, among the intervention effects observed in different studies (this variation is often referred to as tau-squared (τ2, or Tau2)). The amount of variation, and hence the adjustment, can be estimated from the intervention effects and standard errors of the studies included in the meta-analysis.