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.
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.
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.
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.
Additional file 1: Derivation of formulae in Table 1. (DOCX 19 kb)
Additional file 2: Excel template to transform original effect size using the proposed formulae. (XLSX 19 kb)12874_2017_322_MOESM2_ESM.xlsx
Glass GV. Primary, secondary, and meta-analysis of research. Educ Res. 1976;5(10):3–8. CrossRef
Rodriguez-Barranco M, Lacasana M, Aguilar-Garduno C, Alguacil J, Gil F, Gonzalez-Alzaga B, Rojas-Garcia A. Association of arsenic, cadmium and manganese exposure with neurodevelopment and behavioural disorders in children: a systematic review and meta-analysis. Sci Total Environ. 2013;454–455:562–77. CrossRefPubMed
Wechsler D. WISC-IV administration and scoring manual. San Antonio: Harcourt Assessment; 2003.
Hamadani JD, Grantham-McGregor SM, Tofail F, Nermell B, Fangstrom B, Huda SN, Yesmin S, Rahman M, Vera-Hernandez M, Arifeen SE, et al. Pre- and postnatal arsenic exposure and child development at 18 months of age: a cohort study in rural Bangladesh. Int J Epidemiol. 2010;39(5):1206–16. CrossRefPubMed
von Ehrenstein OS, Poddar S, Yuan Y, Mazumder DG, Eskenazi B, Basu A, Hira-Smith M, Ghosh N, Lahiri S, Haque R, et al. Children’s intellectual function in relation to arsenic exposure. Epidemiology. 2007;18(1):44–51. CrossRef
Rothstein HR, Sutton AJ, Borenstein M. (Eds.). Publication bias in meta-analysis: Prevention, assessment and adjustments. New York: John Wiley & Sons; 2006. p. 9–49. ISBN- 0-470-87014-1.
- Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
María José Sánchez
- BioMed Central
Neu im Fachgebiet AINS
Meistgelesene Bücher aus dem Fachgebiet AINS
Mail Icon II