Beyond the funnel plot: editorial and reviewer perspectives on publication bias in systematic reviews and meta-analyses in clinical medicine

Abstract

Publication bias is a fundamental threat to the validity of systematic reviews and meta-analyses in clinical medicine. Yet current practice often reduces its assessment to the mechanical application of funnel plots, asymmetry tests, or single adjustment procedures, with limited attention to the underlying assumptions, alternative explanations, or implications for evidence certainty. This narrative methodological article reframes publication bias assessment as an interpretive and editorial responsibility rather than a purely technical problem. We examine what commonly used methods can and cannot reliably support. Detection tools function as nonspecific stress tests that identify deviations from simplified models; they do not diagnose selective publication but highlight situations in which the underlying assumptions require closer inspection. Adjustment approaches, including trim-and-fill, selection models, and regression-based methods, generate hypothetical estimates under unverifiable assumptions**, and therefore provide** sensitivity analyses rather than corrections that recover the true underlying effect. Divergence across adjustment methods is particularly informative, signaling inferential fragility rather than analytical failure. We identify five recurring misinterpretations encountered in peer review: equating asymmetry with proof of publication bias; privileging bias-adjusted estimates as inherently more credible; relying on a single adjustment method without examining assumption dependence; ignoring the plausibility of adjustment direction and magnitude; and overlooking implications for certainty of evidence. Editors and reviewers should prioritize transparency of assumptions, seriously consider alternative explanations, and calibrate conclusions proportionately. Viewing publication bias assessment as an interpretive responsibility rather than a methodological checklist promotes more disciplined inference and strengthens trust in clinical evidence synthesis.