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Standardizing effect size from linear regression models with log-transformed variables for meta-analysis

Overview of attention for article published in BMC Medical Research Methodology, March 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

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Title
Standardizing effect size from linear regression models with log-transformed variables for meta-analysis
Published in
BMC Medical Research Methodology, March 2017
DOI 10.1186/s12874-017-0322-8
Pubmed ID
Authors

Miguel Rodríguez-Barranco, Aurelio Tobías, Daniel Redondo, Elena Molina-Portillo, María José Sánchez

Abstract

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.

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X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 85 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 16%
Student > Ph. D. Student 13 15%
Student > Master 11 13%
Professor 5 6%
Student > Doctoral Student 4 5%
Other 14 16%
Unknown 24 28%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 7%
Psychology 5 6%
Medicine and Dentistry 5 6%
Economics, Econometrics and Finance 5 6%
Environmental Science 4 5%
Other 23 27%
Unknown 37 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 March 2017.
All research outputs
#4,701,993
of 22,959,818 outputs
Outputs from BMC Medical Research Methodology
#747
of 2,027 outputs
Outputs of similar age
#90,945
of 333,987 outputs
Outputs of similar age from BMC Medical Research Methodology
#16
of 49 outputs
Altmetric has tracked 22,959,818 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,027 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 333,987 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.