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Integrating independent spatio‐temporal replications to assess population trends in disease spread

Overview of attention for article published in Statistics in Medicine, July 2016
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Article details
Title
Integrating independent spatio‐temporal replications to assess population trends in disease spread
Published in
Statistics in Medicine, July 2016
DOI 10.1002/sim.7056
Pubmed ID
Authors
Abstract

Glaucoma is the second leading cause of blindness in the USA. A visual field test (perimetry) is used to sample and quantitate visual field function in preselected regions in the eye. These regions can be considered a spatial field with replications across independently measured individuals. At return visits, a new set of visual field measurements is obtained producing a subject specific spatio-temporal dataset. We develop a Bayesian hierarchical modeling framework to analyze these spatio-temporal datasets both for individual level spread and as aggregate population level trends. Our model extends previous research utilizing a dimension reduction matrix and individual specific latent variables. Human characteristics are incorporated into the model to help explain glaucoma progression. One beneficial product of our model is smoothed estimates for individuals. We also specify how progression rates are computed for monitoring purposes so that clinicians can track changes and predict forward in time. Copyright © 2016 John Wiley & Sons, Ltd.

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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 demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 2 13%
Researcher 2 13%
Professor 1 7%
Student > Master 1 7%
Student > Postgraduate 1 7%
Other 0 0%
Unknown 8 53%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 4 27%
Mathematics 1 7%
Computer Science 1 7%
Engineering 1 7%
Unknown 8 53%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 25 July 2016.
All research outputs
#16,707,477
of 24,571,708 outputs
Outputs from Statistics in Medicine
#2,410
of 4,026 outputs
Outputs of similar age
#244,411
of 372,345 outputs
Outputs of similar age from Statistics in Medicine
#31
of 71 outputs
Altmetric has tracked 24,571,708 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,026 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 29th percentile – i.e., 29% 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 372,345 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 71 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 50% of its contemporaries.