Meta-analysis is a powerful analytic method for summarizing effect estimates across studies. However, conventional meta-analysis often assumes a linear exposure-outcome relationship and does not account for variability over the exposure ranges. In this work, we first used simulation techniques to illustrate that the linear-based meta-analytical approach may result in oversimplistic effect estimation based on 3 plausible nonlinear exposure-outcome curves (S-shape, inverted U-shape, and M-shape). We showed that subgroup meta-analysis that stratifies on exposure levels can investigate nonlinearity and identify the consistency of effect magnitudes in these simulated examples. Next, we examined the heterogeneity of effect estimates across exposure ranges in 2 published linear-based meta-analyses of prenatal exposure to per- and polyfluoroalkyl substances (PFAS) on changes in mean birth weight or risk of preterm birth. The reanalysis found some varying effect sizes and potential heterogeneity when restricting to different PFAS exposure ranges, but findings were sensitive to the cut-off choices used to rank the exposure levels. Finally, we discussed methodological challenges and recommendations for detecting and interpreting potential nonlinear associations in meta-analysis. Using meta-analysis without accounting for exposure range could contribute to literature inconsistency for exposure-induced health effects and impede evidence-based policymaking. Therefore, investigating result heterogeneity by exposure range is recommended. This article is part of a Special Collection on Environmental Epidemiology.
Keywords: dose-response; endocrine disruptors; environmental exposure; meta-analysis; persistent organic pollutant; pregnancy.
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