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Causal diagrams and the logic of matched case-control studies

Overview of attention for article published in Clinical Epidemiology, May 2012
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
41 Mendeley
citeulike
1 CiteULike
Title
Causal diagrams and the logic of matched case-control studies
Published in
Clinical Epidemiology, May 2012
DOI 10.2147/clep.s31271
Pubmed ID
Authors

Eyal Shahar, Shahar

Abstract

It is tempting to assume that confounding bias is eliminated by choosing controls that are identical to the cases on the matched confounder(s). We used causal diagrams to explain why such matching not only fails to remove confounding bias, but also adds colliding bias, and why both types of bias are removed by conditioning on the matched confounder(s). As in some publications, we trace the logic of matching to a possible tradeoff between effort and variance, not between effort and bias. Lastly, we explain why the analysis of a matched case-control study - regardless of the method of matching - is not conceptually different from that of an unmatched study.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Spain 1 2%
Canada 1 2%
Japan 1 2%
Brazil 1 2%
Unknown 35 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 32%
Student > Ph. D. Student 6 15%
Professor 5 12%
Professor > Associate Professor 4 10%
Student > Master 4 10%
Other 7 17%
Unknown 2 5%
Readers by discipline Count As %
Medicine and Dentistry 21 51%
Agricultural and Biological Sciences 3 7%
Environmental Science 2 5%
Biochemistry, Genetics and Molecular Biology 2 5%
Social Sciences 2 5%
Other 4 10%
Unknown 7 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 08 February 2021.
All research outputs
#8,967,211
of 16,983,585 outputs
Outputs from Clinical Epidemiology
#252
of 554 outputs
Outputs of similar age
#62,392
of 132,901 outputs
Outputs of similar age from Clinical Epidemiology
#8
of 23 outputs
Altmetric has tracked 16,983,585 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 554 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.8. This one has gotten more attention than average, scoring higher than 53% 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 132,901 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 52% of its contemporaries.
We're also able to compare this research output to 23 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 65% of its contemporaries.