Quantifying heterogeneity in individual participant data meta-analysis with binary outcomes

BackgroundIn meta-analyses (MA), effect estimates that are pooled together will often be heterogeneous. Determining how substantial heterogeneity is is an important aspect of MA.MethodWe consider how best to quantify heterogeneity in the context of individual participant data meta-analysis (IPD-MA) of binary data. Both two- and one-stage approaches are evaluated via simulation study. We consider conventional I2 and R2 statistics estimated via a two-stage approach and R2 estimated via a one-stage approach. We propose a simulation-based intraclass correlation coefficient (ICC) adapted from Goldstein et al. to estimate the I2, from the one-stage approach.ResultsResults show that when there is no effect modification, the estimated I2 from the two-stage model is underestimated, while in the one-stage model, it is overestimated. In the presence of effect modification, the estimated I2 from the one-stage model has better performance than that from the two-stage model when the prevalence of the outcome is high. The I2 from the two-stage model is less sensitive to the strength of effect modification when the number of studies is large and prevalence is low.ConclusionsThe simulation-based I2 based on a one-stage approach has better performance than the conventional I2 based on a two-stage approach when there is strong effect modification with high prevalence.