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Collider scope: when selection bias can substantially influence observed associations

Overview of attention for article published in International Journal of Epidemiology, September 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

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57 tweeters
facebook
2 Facebook pages

Citations

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38 Dimensions

Readers on

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99 Mendeley
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Title
Collider scope: when selection bias can substantially influence observed associations
Published in
International Journal of Epidemiology, September 2017
DOI 10.1093/ije/dyx206
Pubmed ID
Authors

Marcus R Munafò, Kate Tilling, Amy E Taylor, David M Evans, George Davey Smith

Abstract

Large-scale cross-sectional and cohort studies have transformed our understanding of the genetic and environmental determinants of health outcomes. However, the representativeness of these samples may be limited-either through selection into studies, or by attrition from studies over time. Here we explore the potential impact of this selection bias on results obtained from these studies, from the perspective that this amounts to conditioning on a collider (i.e. a form of collider bias). Whereas it is acknowledged that selection bias will have a strong effect on representativeness and prevalence estimates, it is often assumed that it should not have a strong impact on estimates of associations. We argue that because selection can induce collider bias (which occurs when two variables independently influence a third variable, and that third variable is conditioned upon), selection can lead to substantially biased estimates of associations. In particular, selection related to phenotypes can bias associations with genetic variants associated with those phenotypes. In simulations, we show that even modest influences on selection into, or attrition from, a study can generate biased and potentially misleading estimates of both phenotypic and genotypic associations. Our results highlight the value of knowing which population your study sample is representative of. If the factors influencing selection and attrition are known, they can be adjusted for. For example, having DNA available on most participants in a birth cohort study offers the possibility of investigating the extent to which polygenic scores predict subsequent participation, which in turn would enable sensitivity analyses of the extent to which bias might distort estimates.

Twitter Demographics

The data shown below were collected from the profiles of 57 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 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 99 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 10%
Student > Ph. D. Student 4 4%
Student > Master 4 4%
Unspecified 3 3%
Student > Doctoral Student 2 2%
Other 3 3%
Unknown 73 74%
Readers by discipline Count As %
Medicine and Dentistry 7 7%
Unspecified 6 6%
Biochemistry, Genetics and Molecular Biology 4 4%
Computer Science 1 1%
Nursing and Health Professions 1 1%
Other 7 7%
Unknown 73 74%

Attention Score in Context

This research output has an Altmetric Attention Score of 31. 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 02 November 2018.
All research outputs
#444,824
of 12,236,619 outputs
Outputs from International Journal of Epidemiology
#270
of 3,915 outputs
Outputs of similar age
#20,114
of 271,146 outputs
Outputs of similar age from International Journal of Epidemiology
#6
of 52 outputs
Altmetric has tracked 12,236,619 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,915 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 done particularly well, scoring higher than 93% 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 271,146 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.