1. Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
- Author
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María José Sánchez, Elena Molina-Portillo, Daniel Redondo, Miguel Rodríguez-Barranco, Aurelio Tobias, Tobías, Aurelio, [Rodríguez-Barranco,M, Redondo,D, Molina-Portillo,E, Sánchez, MJ] Andalusian School of Public Health (EASP), Granada, Spain. [Rodríguez-Barranco,M, Sánchez, MJ] Instituto de Investigación Biosanitaria ibs. GRANADA, University Hospitals of Granada/University of Granada, Granada, Spain. [Rodríguez-Barranco,M, Sánchez, MJ] CIBERESP, Madrid, Spain. [Tobías,A] Institute of Environmental Assessment and Water Research (IDAEA), Spanish Council for Scientific Research (CSIC), Barcelona, Spain, and Tobías, Aurelio [0000-0001-6428-6755]
- Subjects
Revisión ,Proper linear model ,Log-transformation ,Epidemiology ,media_common.quotation_subject ,Design matrix ,Health Informatics ,010501 environmental sciences ,Effect size ,01 natural sciences ,Arsenic ,03 medical and health sciences ,0302 clinical medicine ,Modelos estadísticos ,Publication Type::Publication Formats::Review [Medical Subject Headings] ,Meta-Analysis as Topic ,Linear predictor function ,Statistics ,Regression coefficients ,Humans ,Computer Simulation ,030212 general & internal medicine ,Modelos lineales ,Segmented regression ,Linear regression ,Metaanálisis ,Chemicals and Drugs::Inorganic Chemicals::Elements::Metals, Heavy::Manganese [Medical Subject Headings] ,0105 earth and related environmental sciences ,Mathematics ,media_common ,Chemicals and Drugs::Inorganic Chemicals::Elements::Arsenic [Medical Subject Headings] ,lcsh:R5-920 ,Manganese ,Variables ,Regression analysis ,Information Science::Information Science::Computing Methodologies::Computer Simulation [Medical Subject Headings] ,Meta-analysis ,Publication Type::Study Characteristics::Meta-Analysis [Medical Subject Headings] ,Neurodevelopmental Disorders ,Linear Models ,Systematic review ,Errors-in-variables models ,Erratum ,lcsh:Medicine (General) ,Regression diagnostic ,Health Care::Environment and Public Health::Public Health::Epidemiologic Methods::Statistics as Topic::Regression Analysis::Linear Models [Medical Subject Headings] ,Hair - 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. © 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.
- Published
- 2017