Spurious precision in meta-analysis of observational research

Vista sencilla de ítem
dc.contributor.authorIrsova, Zuzana
dc.contributor.authorBom, Pedro
dc.contributor.authorHavránek, Tomáš
dc.contributor.authorRachinger, Heiko
dc.date.accessioned2025-10-14T13:26:23Z
dc.date.available2025-10-14T13:26:23Z
dc.date.issued2025-09-26
dc.date.updated2025-10-14T13:26:23Z
dc.description.abstractMeta-analysis assigns more weight to studies with smaller standard errors to maximize the precision of the overall estimate. In observational settings, however, standard errors are shaped by methodological decisions. These decisions can interact with publication bias and p-hacking, potentially leading to spuriously precise results reported by primary studies. Here we show that such spurious precision undermines standard meta-analytic techniques, including inverse-variance weighting and bias corrections based on the funnel plot. Through simulations and large-scale empirical applications, we find that selection models do not resolve the issue. In some cases, a simple unweighted mean of reported estimates outperforms widely used correction methods. We introduce MAIVE (Meta-Analysis Instrumental Variable Estimator), an approach that reduces bias by using sample size as an instrument for reported precision. MAIVE offers a simple and robust solution for improving the reliability of meta-analyses in the presence of spurious precision.en
dc.description.sponsorshipH.R. acknowledge support from the Czech Science Foundation (grant no. 23-05227M). P.R.D.B. also acknowledges support from the Basque Government Department of Education (grant no. IT1429-22). H.R. also acknowledges support from the Spanish Ministry of Science and Innovation (grant no. PID2020-114646RB-C43). P.R.D.B. and H.R. acknowledge support under grant PID2023-152916NB-I00 financed by MCIN/AEI/10.13039/501100011033. T.H. acknowledges support from the Czech Science Foundation (grant no. 24-11583S) and from the Institute for Research on the Socioeconomic Impact of Diseases and Systemic Risks (grant no. LX22NPO5101), funded by the European Union–Next Generation EUen
dc.identifier.citationIrsova, Z., Bom, P. R. D., Havranek, T., & Rachinger, H. (2025). Spurious precision in meta-analysis of observational research. Nature Communications , 16(1). https://doi.org/10.1038/S41467-025-63261-0
dc.identifier.doi10.1038/S41467-025-63261-0
dc.identifier.eissn2041-1723
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3959
dc.language.isoeng
dc.publisherNature Research
dc.rights© The Author(s) 2025
dc.titleSpurious precision in meta-analysis of observational researchen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue1
oaire.citation.titleNature Communications
oaire.citation.volume16
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
irsova_spurious_2025.pdf
Tamaño:
731.73 KB
Formato:
Adobe Portable Document Format
Descargar
Colecciones
Artículos