Nonlinear effects and effect modification at the participant-level in IPD meta-analysis part 2: methodological guidance is available

Objectives: To review methodological guidance for nonlinear covariate-outcome associations (NL), and linear effect modification and nonlinear effect modification (LEM and NLEM) at the participant level in individual participant data meta-analyses (IPDMAs) and their power requirements.Study Design and Setting: We searched Medline, Embase, Web of Science, Scopus, PsycINFO and the Cochrane Library to identify methodology publications on IPDMA of LEM, NL or NLEM (PROSPERO CRD42019126768).Results: Through screening 6,466 records we identified 54 potential articles of which 23 full texts were relevant. Nine further relevant publications were published before or after the literature search and were added. Of these 32 references, 21 articles considered LEM, 6 articles NL or NLEM and 6 articles described sample size calculations. A book described all four. Sample size may be calculated through simulation or closed form. Assessments of LEM or NLEM at the participant level need to be based on within-trial information alone. Nonlinearity (NL or NLEM) can be modeled using polynomials or splines to avoid categorization.Conclusion: Detailed methodological guidance on IPDMA of effect modification at participant-level is available. However, methodology papers for sample size and nonlinearity are rarer and may not cover all scenarios. On these aspects, further guidance is needed.(c) 2023 The Authors. Published by Elsevier Inc.This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

Automated summary

This article reviews the methodological guidance for nonlinear covariate-outcome associations, linear effect modification, and nonlinear effect modification in individual participant data meta-analyses. The study found that detailed methodological guidance on effect modification at the participant level is available, but there is still a need for further research on sample size and nonlinearity.