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Log-binomial models: exploring failed convergence

Overview of attention for article published in Emerging Themes in Epidemiology, December 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#48 of 155)
  • High Attention Score compared to outputs of the same age (83rd percentile)

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13 X users
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171 Mendeley
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Title
Log-binomial models: exploring failed convergence
Published in
Emerging Themes in Epidemiology, December 2013
DOI 10.1186/1742-7622-10-14
Pubmed ID
Authors

Tyler Williamson, Misha Eliasziw, Gordon Hilton Fick

Abstract

Relative risk is a summary metric that is commonly used in epidemiological investigations. Increasingly, epidemiologists are using log-binomial models to study the impact of a set of predictor variables on a single binary outcome, as they naturally offer relative risks. However, standard statistical software may report failed convergence when attempting to fit log-binomial models in certain settings. The methods that have been proposed in the literature for dealing with failed convergence use approximate solutions to avoid the issue. This research looks directly at the log-likelihood function for the simplest log-binomial model where failed convergence has been observed, a model with a single linear predictor with three levels. The possible causes of failed convergence are explored and potential solutions are presented for some cases.

X Demographics

X Demographics

The data shown below were collected from the profiles of 13 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 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 2%
United Kingdom 1 <1%
Brazil 1 <1%
Unknown 166 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 32 19%
Student > Ph. D. Student 31 18%
Researcher 24 14%
Student > Doctoral Student 15 9%
Other 11 6%
Other 27 16%
Unknown 31 18%
Readers by discipline Count As %
Medicine and Dentistry 46 27%
Social Sciences 18 11%
Nursing and Health Professions 13 8%
Agricultural and Biological Sciences 11 6%
Mathematics 10 6%
Other 23 13%
Unknown 50 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 2023.
All research outputs
#4,798,839
of 25,366,663 outputs
Outputs from Emerging Themes in Epidemiology
#48
of 155 outputs
Outputs of similar age
#52,881
of 321,618 outputs
Outputs of similar age from Emerging Themes in Epidemiology
#1
of 2 outputs
Altmetric has tracked 25,366,663 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 155 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.2. This one has gotten more attention than average, scoring higher than 69% 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 321,618 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 2 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