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Background  There is increasing awareness that meta-analyses require a sufficiently large information size to detect or reject an anticipated intervention effect. The required information size in a meta-analysis may be calculated from an anticipated  a priori  intervention effect or from an intervention effect suggested by trials with low-risk of bias.    Methods  Information size calculations need to consider the total model variance in a meta-analysis to control type I and type II errors. Here, we derive an adjusting factor for the required information size under any random-effects model meta-analysis.    Results  We devise a measure of diversity ( D  2 ) in a meta-analysis, which is the relative variance reduction when the meta-analysis model is changed from a random-effects into a fixed-effect model.  D  2  is the percentage that the between-trial variability constitutes of the sum of the between-trial variability and a sampling error estimate considering the required information size.  D  2  is different from the intuitively obvious adjusting factor based on the common quantification of heterogeneity, the inconsistency ( I  2 ), which may underestimate the required information size. Thus,  D  2  and  I  2  are compared and interpreted using several simulations and clinical examples. In addition we show mathematically that diversity is equal to or greater than inconsistency, that is  D  2  ≥  I  2 , for all meta-analyses.    Conclusion  We conclude that  D  2  seems a better alternative than  I  2  to consider model variation in any random-effects meta-analysis despite the choice of the between trial variance estimator that constitutes the model. Furthermore,  D  2  can readily adjust the required information size in any random-effects model meta-analysis.

Estimating required information size by quantifying diversity in random-effects model meta-analyses