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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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2 X users

Citations

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65 Dimensions

Readers on

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125 Mendeley
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1 CiteULike
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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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Spain 1 <1%
United States 1 <1%
Belgium 1 <1%
Unknown 121 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 22%
Student > Master 19 15%
Researcher 16 13%
Other 11 9%
Professor 7 6%
Other 22 18%
Unknown 23 18%
Readers by discipline Count As %
Medicine and Dentistry 46 37%
Agricultural and Biological Sciences 7 6%
Environmental Science 6 5%
Mathematics 4 3%
Nursing and Health Professions 4 3%
Other 23 18%
Unknown 35 28%
Attention Score in Context

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
#14,743,944
of 22,694,633 outputs
Outputs from BMC Medical Research Methodology
#1,435
of 2,001 outputs
Outputs of similar age
#104,543
of 172,679 outputs
Outputs of similar age from BMC Medical Research Methodology
#17
of 29 outputs
Altmetric has tracked 22,694,633 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,001 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one is in the 25th percentile – i.e., 25% 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 172,679 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.