The Interpretation of Random-Effects Meta-Analysis in Decision Models

By A. Ades, Guobing Lu, Julian P. T. Higgins. Medical decision making, 2005

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A re-evaluation of random-effects meta-analysis (2009, background): “This may be the parameter of primary interest: for example, the average efficacy of a treatment may be the most relevant parameter for health care providers (Feinstein, 1995; Light, 1987; Ades et al., 2005).”
Predictive distributions for between-study heterogeneity and simple methods for their application in Bayesian meta-analysis (2014, background): “A Bayesian random-effects meta-analysis is advantageous in allowing researchers to incorporate external evidence on the likely extent of between-study heterogeneity in a particular research setting, to reduce the imprecision [3, 4], as well as facilitating prediction of effects in future studies and”
Reasons or excuses for avoiding meta-analysis in forest plots (2008, background): “Models that can accommodate statistical heterogeneity between studies include traditional random effects (models that assume that different studies have different true treatment effects),”
Interpretation of random effects meta-analyses (2011, background): “Even when factors causing heterogeneity are identified, unexplained heterogeneity may remain. Thus random effects meta-analysis, which accounts for unexplained heterogeneity, will continue to be prominent in the medical literature. Including a prediction interval, which indicates the possible treatm”