Interpretation of random effects meta-analyses

By R. Riley, Julian P. T. Higgins, J. Deeks. British medical journal, 2011

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Visualizing Assumptions and Results in Network Meta-analysis: The Network Graphs Package (2015, background): “The extent of uncertainty in the estimated treatment eﬀects in meta-analysis is re-ﬂected not only by the conﬁdence intervals but also by the predictive intervals that incorporate the extent of heterogeneity (Higgins, Thompson, and Spiegelhalter 2009; Riley, Higgins, and Deeks 2011).”
The trim-and-fill method for publication bias: practical guidelines and recommendations based on a large database of meta-analyses (2019, methodology): “random-effects meta-analysis model, such as the DerSimonian– Laird method,([30]) is used to incorporate between-study heterogeneity in the overall effect estimate.([31])”
Introduction, comparison, and validation of Meta‐Essentials: A free and simple tool for meta‐analysis (2017, methodology): “Therefore, the prediction interval accurately embraces the notion of heterogeneity and the dispersion of true effects.(38) Meta‐Essentials provides the prediction interval by default and automatically includes it in the forest plot (see the green line in Figure 1).”
Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression (2012, methodology): “d+bdi, can be compared by instead drawing d b from N d;Vþ Σ̂ (Higgins et al., 2009; Riley et al., 2011).”