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Combining directed acyclic graphs and the change-in-estimate procedure as a novel approach to adjustment-variable selection in epidemiology

Overview of attention for article published in BMC Medical Research Methodology, October 2012
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Mentioned by

twitter
2 tweeters

Citations

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36 Dimensions

Readers on

mendeley
86 Mendeley
citeulike
1 CiteULike
Title
Combining directed acyclic graphs and the change-in-estimate procedure as a novel approach to adjustment-variable selection in epidemiology
Published in
BMC Medical Research Methodology, October 2012
DOI 10.1186/1471-2288-12-156
Pubmed ID
Authors

David Evans, Basile Chaix, Thierry Lobbedez, Christian Verger, Antoine Flahault

Abstract

Directed acyclic graphs (DAGs) are an effective means of presenting expert-knowledge assumptions when selecting adjustment variables in epidemiology, whereas the change-in-estimate procedure is a common statistics-based approach. As DAGs imply specific empirical relationships which can be explored by the change-in-estimate procedure, it should be possible to combine the two approaches. This paper proposes such an approach which aims to produce well-adjusted estimates for a given research question, based on plausible DAGs consistent with the data at hand, combining prior knowledge and standard regression methods.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 1%
United Kingdom 1 1%
Argentina 1 1%
Belgium 1 1%
Spain 1 1%
United States 1 1%
Unknown 80 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 27%
Student > Master 11 13%
Professor > Associate Professor 8 9%
Researcher 8 9%
Professor 5 6%
Other 15 17%
Unknown 16 19%
Readers by discipline Count As %
Medicine and Dentistry 28 33%
Agricultural and Biological Sciences 6 7%
Nursing and Health Professions 5 6%
Environmental Science 4 5%
Mathematics 3 3%
Other 16 19%
Unknown 24 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 01 April 2019.
All research outputs
#7,823,427
of 13,568,727 outputs
Outputs from BMC Medical Research Methodology
#806
of 1,251 outputs
Outputs of similar age
#113,153
of 241,999 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 1 outputs
Altmetric has tracked 13,568,727 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,251 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.9. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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 241,999 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 50% 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