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10.1186/s12874-017-0322-8 http://scihub22266oqcxt.onion/10.1186/s12874-017-0322-8 C5356327!5356327 !28302052     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28302052 &cmd=llinks): Failed to open stream: HTTP request failed! 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Standardizing effect size from linear regression models with log-transformed variables for meta-analysis |pdf=|usr=}}{{28302052 }} gab.com Text Standardizing effect size from linear regression models with log-transformed variables for meta-analysis JO: BMC Med Res Methodol http://www.kidney.de/pget.php?&tw=1&pmid=28302052 #FOAvip 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. Twit Text FOAVip Standardizing effect size from linear regression models with log-transformed variables for meta-analysis ????BMC Med Res Methodol http://www.kidney.de/pget.php?&tw=1&pmid=28302052 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28302052 #FOAvip English Wikipedia <ref>{{Cite pmid|28302052 }}</ref> Standardizing effect size from linear regression models with log-transformed variables for meta-analysis #MMPMID28302052 Rodríguez-Barranco M ; Tobías A ; Redondo D ; Molina-Portillo E ; Sánchez MJ BMC Med Res Methodol 2017[Mar]; 17 (1 ): 44 PMID28302052 show ga 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. |*Linear Models [MESH] |*Meta-Analysis as Topic [MESH] |Arsenic/*toxicity/urine [MESH] |Computer Simulation [MESH] |Hair/chemistry [MESH] |Humans [MESH] |Manganese/*toxicity [MESH]Standardizing effect size from linear regression models with log-trans(epmc).pdf DeepDyve Pubget Overpricing