A Nonparametric "Trim and Fill" Method of Accounting for Publication Bias in Meta-Analysis

DOI10.2307/2669529zbMath1011.62112OpenAlexW4235061535MaRDI QIDQ4541281

Publication date: 30 July 2002

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/2669529

zbMATH Keywords

passive smoking data augmentation meta-analysis lung cancer publication bias funnel plots file drawer problem missing studies

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Nonparametric estimation (62G05)

Related Items (16)

EMBRACE: An EM‐based bias reduction approach through Copas‐model estimation for quantifying the evidence of selective publishing in network meta‐analysis ⋮ Statistical exploration of publication bias ⋮ Detecting and adjusting for small‐study effects in meta‐analysis ⋮ Diagnostic test meta-analysis by empirical likelihood under a Copas-like selection model ⋮ Imputation of truncated \(p\)-values for meta-analysis methods and its genomic application ⋮ Treatment-effect estimates adjusted for small-study effects via a limit meta-analysis ⋮ Trim and Fill: A Simple Funnel‐Plot–Based Method of Testing and Adjusting for Publication Bias in Meta‐Analysis ⋮ Publication Bias and Meta-Analysis for 2×2 Tables: An Average Markov Chain Monte Carlo EM Algorithm ⋮ Adjusted likelihoods for synthesizing empirical evidence from studies that differ in quality and design: effects of environmental tobacco smoke ⋮ Meta‐Analysis of Studies with Missing Data ⋮ Assessing the Sensitivity of Meta-analysis to Selection Bias: A Multiple Imputation Approach ⋮ Empirical likelihood meta-analysis with publication bias correction under copas-like selection model ⋮ Infinite diameter confidence sets in Hedges' publication bias model ⋮ Publication bias in meta-analysis: confidence intervals for Rosenthal's fail-safe number ⋮ Using Journal Impact Factors to Correct for the Publication Bias of Medical Studies ⋮ Copas' method is sensitive to different mechanisms of publication bias

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