Cargando…

Most published meta-regression analyses based on aggregate data suffer from methodological pitfalls: a meta-epidemiological study

BACKGROUND: Due to clinical and methodological diversity, clinical studies included in meta-analyses often differ in ways that lead to differences in treatment effects across studies. Meta-regression analysis is generally recommended to explore associations between study-level characteristics and tr...

Descripción completa

Detalles Bibliográficos
Autores principales: Geissbühler, Michael, Hincapié, Cesar A., Aghlmandi, Soheila, Zwahlen, Marcel, Jüni, Peter, da Costa, Bruno R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8207572/
https://www.ncbi.nlm.nih.gov/pubmed/34130658
http://dx.doi.org/10.1186/s12874-021-01310-0
_version_ 1783708798343446528
author Geissbühler, Michael
Hincapié, Cesar A.
Aghlmandi, Soheila
Zwahlen, Marcel
Jüni, Peter
da Costa, Bruno R.
author_facet Geissbühler, Michael
Hincapié, Cesar A.
Aghlmandi, Soheila
Zwahlen, Marcel
Jüni, Peter
da Costa, Bruno R.
author_sort Geissbühler, Michael
collection PubMed
description BACKGROUND: Due to clinical and methodological diversity, clinical studies included in meta-analyses often differ in ways that lead to differences in treatment effects across studies. Meta-regression analysis is generally recommended to explore associations between study-level characteristics and treatment effect, however, three key pitfalls of meta-regression may lead to invalid conclusions. Our aims were to determine the frequency of these three pitfalls of meta-regression analyses, examine characteristics associated with the occurrence of these pitfalls, and explore changes between 2002 and 2012. METHODS: A meta-epidemiological study of studies including aggregate data meta-regression analysis in the years 2002 and 2012. We assessed the prevalence of meta-regression analyses with at least 1 of 3 pitfalls: ecological fallacy, overfitting, and inappropriate methods to regress treatment effects against the risk of the analysed outcome. We used logistic regression to investigate study characteristics associated with pitfalls and examined differences between 2002 and 2012. RESULTS: Our search yielded 580 studies with meta-analyses, of which 81 included meta-regression analyses with aggregated data. 57 meta-regression analyses were found to contain at least one pitfall (70%): 53 were susceptible to ecological fallacy (65%), 14 had a risk of overfitting (17%), and 5 inappropriately regressed treatment effects against the risk of the analysed outcome (6%). We found no difference in the prevalence of meta-regression analyses with methodological pitfalls between 2002 and 2012, nor any study-level characteristic that was clearly associated with the occurrence of any of the pitfalls. CONCLUSION: The majority of meta-regression analyses based on aggregate data contain methodological pitfalls that may result in misleading findings. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-021-01310-0.
format Online
Article
Text
id pubmed-8207572
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-82075722021-06-16 Most published meta-regression analyses based on aggregate data suffer from methodological pitfalls: a meta-epidemiological study Geissbühler, Michael Hincapié, Cesar A. Aghlmandi, Soheila Zwahlen, Marcel Jüni, Peter da Costa, Bruno R. BMC Med Res Methodol Research BACKGROUND: Due to clinical and methodological diversity, clinical studies included in meta-analyses often differ in ways that lead to differences in treatment effects across studies. Meta-regression analysis is generally recommended to explore associations between study-level characteristics and treatment effect, however, three key pitfalls of meta-regression may lead to invalid conclusions. Our aims were to determine the frequency of these three pitfalls of meta-regression analyses, examine characteristics associated with the occurrence of these pitfalls, and explore changes between 2002 and 2012. METHODS: A meta-epidemiological study of studies including aggregate data meta-regression analysis in the years 2002 and 2012. We assessed the prevalence of meta-regression analyses with at least 1 of 3 pitfalls: ecological fallacy, overfitting, and inappropriate methods to regress treatment effects against the risk of the analysed outcome. We used logistic regression to investigate study characteristics associated with pitfalls and examined differences between 2002 and 2012. RESULTS: Our search yielded 580 studies with meta-analyses, of which 81 included meta-regression analyses with aggregated data. 57 meta-regression analyses were found to contain at least one pitfall (70%): 53 were susceptible to ecological fallacy (65%), 14 had a risk of overfitting (17%), and 5 inappropriately regressed treatment effects against the risk of the analysed outcome (6%). We found no difference in the prevalence of meta-regression analyses with methodological pitfalls between 2002 and 2012, nor any study-level characteristic that was clearly associated with the occurrence of any of the pitfalls. CONCLUSION: The majority of meta-regression analyses based on aggregate data contain methodological pitfalls that may result in misleading findings. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-021-01310-0. BioMed Central 2021-06-15 /pmc/articles/PMC8207572/ /pubmed/34130658 http://dx.doi.org/10.1186/s12874-021-01310-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Geissbühler, Michael
Hincapié, Cesar A.
Aghlmandi, Soheila
Zwahlen, Marcel
Jüni, Peter
da Costa, Bruno R.
Most published meta-regression analyses based on aggregate data suffer from methodological pitfalls: a meta-epidemiological study
title Most published meta-regression analyses based on aggregate data suffer from methodological pitfalls: a meta-epidemiological study
title_full Most published meta-regression analyses based on aggregate data suffer from methodological pitfalls: a meta-epidemiological study
title_fullStr Most published meta-regression analyses based on aggregate data suffer from methodological pitfalls: a meta-epidemiological study
title_full_unstemmed Most published meta-regression analyses based on aggregate data suffer from methodological pitfalls: a meta-epidemiological study
title_short Most published meta-regression analyses based on aggregate data suffer from methodological pitfalls: a meta-epidemiological study
title_sort most published meta-regression analyses based on aggregate data suffer from methodological pitfalls: a meta-epidemiological study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8207572/
https://www.ncbi.nlm.nih.gov/pubmed/34130658
http://dx.doi.org/10.1186/s12874-021-01310-0
work_keys_str_mv AT geissbuhlermichael mostpublishedmetaregressionanalysesbasedonaggregatedatasufferfrommethodologicalpitfallsametaepidemiologicalstudy
AT hincapiecesara mostpublishedmetaregressionanalysesbasedonaggregatedatasufferfrommethodologicalpitfallsametaepidemiologicalstudy
AT aghlmandisoheila mostpublishedmetaregressionanalysesbasedonaggregatedatasufferfrommethodologicalpitfallsametaepidemiologicalstudy
AT zwahlenmarcel mostpublishedmetaregressionanalysesbasedonaggregatedatasufferfrommethodologicalpitfallsametaepidemiologicalstudy
AT junipeter mostpublishedmetaregressionanalysesbasedonaggregatedatasufferfrommethodologicalpitfallsametaepidemiologicalstudy
AT dacostabrunor mostpublishedmetaregressionanalysesbasedonaggregatedatasufferfrommethodologicalpitfallsametaepidemiologicalstudy