Prediction Intervals in Meta-analysis: A Clearer View of Heterogeneity and Expected Future Findings Using Stata

Meta-analyses in epidemiology often rely on 95% confidence intervals (CIs) to summarize the precision of pooled estimates. However, CIs are frequently misinterpreted and offer limited insight into how study results vary (heterogeneity) or what future studies might show. Prediction intervals (PIs), by contrast, directly reflect such between-study variability and estimate the range within which the true effect of a future study is expected to fall—providing a more interpretable and policy-relevant view of uncertainty. This talk presents the rationale for using PIs in metaanalyses of odds ratios (ORs), drawing on the methods described in Borenstein’s widely used text on the subject. PIs will be contrasted with traditional heterogeneity measures like I², which is often misused or overinterpreted as a precise index of inconsistency. In addition, PIs allow framing heterogeneity in terms of expected future effects and provides a more intuitive and decision-relevant perspective. Using Stata, I will demonstrate how to compute and visualize PIs, including enhanced graphical methods based on probability density functions (PDFs). Such plots go beyond Stata’s whisker-like default PI displays in forest plots by better illustrating both the expected range and the relative likelihood of future effect sizes—conveying direction, dispersion, and uncertainty in a single visual. Attendees will gain a practical and conceptual understanding of how PIs can complement or even surpass CIs and I² as tools for interpreting and applying meta-analytic evidence in epidemiology