Please use this identifier to cite or link to this item: http://hdl.handle.net/10668/2888
Title: Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
Authors: Rodríguez-Barranco, Miguel
Tobías, Aurelio
Redondo, Daniel
Molina-Portillo, Elena
Sánchez, María José
metadata.dc.contributor.authoraffiliation: [Rodríguez-Barranco,M; Redondo,D; Molina-Portillo,E; Sánchez, MJ] Andalusian School of Public Health (EASP), Granada, Spain. [Rodríguez-Barranco,M; Redondo,D; Molina-Portillo,E; Sánchez, MJ] Instituto de Investigación Biosanitaria ibs. GRANADA, University Hospitals of Granada/University of Granada, Granada, Spain. [Rodríguez-Barranco,M; Redondo,D; Molina-Portillo,E; Sánchez, MJ] CIBERESP, Madrid, Spain. [Tobías,A] Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Barcelona, Spain
Keywords: Meta-analysis;Systematic review;Log-transformation;Linear regression;Effect size;Regression coefficients;Metaanálisis;Revisión;Modelos lineales;Modelos estadísticos
metadata.dc.subject.mesh: Medical Subject Headings::Health Care::Environment and Public Health::Public Health::Epidemiologic Methods::Statistics as Topic::Regression Analysis::Linear Models
Medical Subject Headings::Publication Type::Study Characteristics::Meta-Analysis
Medical Subject Headings::Publication Type::Publication Formats::Review
Medical Subject Headings::Chemicals and Drugs::Inorganic Chemicals::Elements::Arsenic
Medical Subject Headings::Chemicals and Drugs::Inorganic Chemicals::Elements::Metals, Heavy::Manganese
Medical Subject Headings::Information Science::Information Science::Computing Methodologies::Computer Simulation
Issue Date: 17-Mar-2017
Publisher: Biomed Central
Citation: Rodríguez-Barranco M, Tobias A, Redondo D, Molina-Portillo E, Sánchez-Pérez MJ. Standardizing effect size from linear regression models with log-transformed variables for meta-analysis. BMC Med Res Methodol. 2017 Mar 17:17(1):44
Abstract: 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.
URI: http://hdl.handle.net/10668/2888
metadata.dc.relation.publisherversion: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-017-0322-8
metadata.dc.identifier.doi: 10.1186/s12874-017-0322-8
ISSN: 1471-2288 (Online)
Appears in Collections:01- Artículos - EASP. Escuela Andaluza de Salud Pública

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