RT Journal Article
T1 Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
A1 Rodríguez-Barranco, Miguel
A1 Tobías, Aurelio
A1 Redondo, Daniel
A1 Molina-Portillo, Elena
A1 Sanchez-Perez, Maria-Jose
K1 Meta-analysis
K1 Systematic review
K1 Log-transformation
K1 Linear regression
K1 Effect size
K1 Regression coefficients
K1 Metaanálisis
K1 Revisión
K1 Modelos lineales
K1 Modelos estadísticos
AB BackgroundMeta-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.MethodsWe 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.ResultsIn 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.ConclusionsThe 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.
PB Biomed Central
YR 2017
FD 2017-03-17
LK http://hdl.handle.net/10668/2888
UL http://hdl.handle.net/10668/2888
LA en
NO 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
DS RISalud
RD Apr 17, 2025