Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
Metadatos
Mostrar el registro completo del ítemAutor
Rodríguez Barranco, Miguel; Tobías, Aurelio; Redondon, Daniel; Molina Portillo, Elena; Sánchez Pérez, María JoséEditorial
Biomed Central
Materia
Meta-analysis Systematic review Log-transformation Linear regression Effect size Regression coefficients
Fecha
2017Referencia bibliográfica
Rodríguez Barranco, M.; et al. Standardizing effect size from linear regression models with log-transformed variables for meta-analysis. BMC Medical Research Methodology, 17: 44 (2017). [http://hdl.handle.net/10481/49843]
Resumen
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.