Standardizing effect size from linear regression models with log-transformed variables for meta-analysis

Background: Meta-analysis is very useful to summarize the effect of a treatment or a risk factor for a given disease. Often studies report results based on log-transformed variables in order to achieve the principal assumptions of a linear regression model. If this is the case for some, but not all...

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Autores: Rodríguez-Barranco, Miguel, Tobías, Aurelio, Redondo, Daniel, Molina-Portillo, E.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/174699
Acceso en línea:http://hdl.handle.net/10261/174699
Access Level:acceso abierto
Palabra clave:Effect size
Linear regression
Log-transformation
Meta-analysis
Regression coefficients
Systematic review
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spelling Standardizing effect size from linear regression models with log-transformed variables for meta-analysisRodríguez-Barranco, MiguelTobías, AurelioRedondo, DanielMolina-Portillo, E.Effect sizeLinear regressionLog-transformationMeta-analysisRegression coefficientsSystematic reviewBackground: Meta-analysis is very useful to summarize the effect of a treatment or a risk factor for a given disease. Often studies report results based on log-transformed variables in order to achieve the principal assumptions of a linear regression model. If this is the case for some, but not all studies, the effects need to be homogenized. Methods: We derived a set of formulae to transform absolute changes into relative ones, and vice versa, to allow including all results in a meta-analysis. We applied our procedure to all possible combinations of log-transformed independent or dependent variables. We also evaluated it in a simulation based on two variables either normally or asymmetrically distributed. Results: In all the scenarios, and based on different change criteria, the effect size estimated by the derived set of formulae was equivalent to the real effect size. To avoid biased estimates of the effect, this procedure should be used with caution in the case of independent variables with asymmetric distributions that significantly differ from the normal distribution. We illustrate an application of this procedure by an application to a meta-analysis on the potential effects on neurodevelopment in children exposed to arsenic and manganese. Conclusions: The procedure proposed has been shown to be valid and capable of expressing the effect size of a linear regression model based on different change criteria in the variables. Homogenizing the results from different studies beforehand allows them to be combined in a meta-analysis, independently of whether the transformations had been performed on the dependent and/or independent variables. © 2017 The Author(s).The authors would like to thank Begoña Martínez at the Andalusian School of Public Health, for her comments on and suggestions for the manuscript.Peer reviewedSpringer NatureTobías, Aurelio [0000-0001-6428-6755]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]201920192017info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/174699reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1186/s12874-017-0322-8Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1746992026-05-22T06:33:51Z
dc.title.none.fl_str_mv Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
title Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
spellingShingle Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
Rodríguez-Barranco, Miguel
Effect size
Linear regression
Log-transformation
Meta-analysis
Regression coefficients
Systematic review
title_short Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
title_full Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
title_fullStr Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
title_full_unstemmed Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
title_sort Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
dc.creator.none.fl_str_mv Rodríguez-Barranco, Miguel
Tobías, Aurelio
Redondo, Daniel
Molina-Portillo, E.
author Rodríguez-Barranco, Miguel
author_facet Rodríguez-Barranco, Miguel
Tobías, Aurelio
Redondo, Daniel
Molina-Portillo, E.
author_role author
author2 Tobías, Aurelio
Redondo, Daniel
Molina-Portillo, E.
author2_role author
author
author
dc.contributor.none.fl_str_mv Tobías, Aurelio [0000-0001-6428-6755]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Effect size
Linear regression
Log-transformation
Meta-analysis
Regression coefficients
Systematic review
topic Effect size
Linear regression
Log-transformation
Meta-analysis
Regression coefficients
Systematic review
description Background: Meta-analysis is very useful to summarize the effect of a treatment or a risk factor for a given disease. Often studies report results based on log-transformed variables in order to achieve the principal assumptions of a linear regression model. If this is the case for some, but not all studies, the effects need to be homogenized. Methods: We derived a set of formulae to transform absolute changes into relative ones, and vice versa, to allow including all results in a meta-analysis. We applied our procedure to all possible combinations of log-transformed independent or dependent variables. We also evaluated it in a simulation based on two variables either normally or asymmetrically distributed. Results: In all the scenarios, and based on different change criteria, the effect size estimated by the derived set of formulae was equivalent to the real effect size. To avoid biased estimates of the effect, this procedure should be used with caution in the case of independent variables with asymmetric distributions that significantly differ from the normal distribution. We illustrate an application of this procedure by an application to a meta-analysis on the potential effects on neurodevelopment in children exposed to arsenic and manganese. Conclusions: The procedure proposed has been shown to be valid and capable of expressing the effect size of a linear regression model based on different change criteria in the variables. Homogenizing the results from different studies beforehand allows them to be combined in a meta-analysis, independently of whether the transformations had been performed on the dependent and/or independent variables. © 2017 The Author(s).
publishDate 2017
dc.date.none.fl_str_mv 2017
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/174699
url http://hdl.handle.net/10261/174699
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1186/s12874-017-0322-8
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
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