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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 (73rd percentile)

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11 tweeters
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1 Facebook page

Citations

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21 Dimensions

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57 Mendeley
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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.

Twitter Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 18%
Student > Ph. D. Student 9 16%
Researcher 8 14%
Professor 5 9%
Student > Doctoral Student 5 9%
Other 9 16%
Unknown 11 19%
Readers by discipline Count As %
Medicine and Dentistry 5 9%
Agricultural and Biological Sciences 4 7%
Psychology 3 5%
Pharmacology, Toxicology and Pharmaceutical Science 3 5%
Economics, Econometrics and Finance 3 5%
Other 17 30%
Unknown 22 39%

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
#3,080,321
of 15,920,152 outputs
Outputs from BMC Medical Research Methodology
#518
of 1,497 outputs
Outputs of similar age
#82,371
of 307,072 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 1 outputs
Altmetric has tracked 15,920,152 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,497 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.6. This one has gotten more attention than average, scoring higher than 65% 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 307,072 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 73% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them