Flexible nonparametric assessment of time-lag bias in meta-analysis.

Time-lag bias occurs when studies with larger or statistically significant effects appear in print sooner than those with smaller or null effects, which can inflate early meta-analytic estimates and mislead decision makers. Existing diagnostics typically rely on cumulative meta-analysis or meta-regression of effects on publication time. These approaches are vulnerable to repeated-testing inflation and loss of power when temporal trends are nonlinear. We propose a family of flexible nonparametric statistics that summarize standardized pairwise differences between early and later studies, each emphasizing different magnitudes of temporal contrast, and a hybrid test that adaptively combines evidence across these differences via permutation. The procedure controls type I error under exchangeability of publication order and is sensitive to diverse temporal patterns, including linear trends, early outliers, and oscillations in effect sizes over time. Simulations across 9 scenarios, with and without heterogeneity, show that the hybrid test maintains the nominal error rate and improves statistical power relative to linear meta-regression when temporal trends are nonlinear. In 2 clinical meta-analyses, our method detects pronounced time-lag patterns that standard regression misses. We provide guidance for practice and discuss practical considerations for detecting and interpreting time-lag bias in meta-analyses.