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Modeling sleep fragmentation in sleep hypnograms: An instance of fast, scalable discrete-state, discrete-time analyses

Overview of attention for article published in Computational Statistics & Data Analysis, September 2015
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Article details
Title
Modeling sleep fragmentation in sleep hypnograms: An instance of fast, scalable discrete-state, discrete-time analyses
Published in
Computational Statistics & Data Analysis, September 2015
DOI 10.1016/j.csda.2015.03.001
Pubmed ID
Authors
Abstract

Methods are introduced for the analysis of large sets of sleep study data (hypnograms) using a 5-state 20-transition-type structure defined by the American Academy of Sleep Medicine. Application of these methods to the hypnograms of 5598 subjects from the Sleep Heart Health Study provide: the first analysis of sleep hypnogram data of such size and complexity in a community cohort with a range of sleep-disordered breathing severity; introduce a novel approach to compare 5-state (20-transition-type) to 3-state (6-transition-type) sleep structures to assess information loss from combining sleep state categories; extend current approaches of multivariate survival data analysis to clustered, recurrent event discrete-state discrete-time processes; and provide scalable solutions for data analyses required by the case study. The analysis provides detailed new insights into the association between sleep-disordered breathing and sleep architecture. The example data and both R and SAS code are included in online supplementary materials.

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X Demographics

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 19 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 19 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 4 21%
Student > Bachelor 3 16%
Student > Doctoral Student 2 11%
Student > Master 2 11%
Other 1 5%
Other 2 11%
Unknown 5 26%
Readers by discipline
Readers by discipline Count As %
Engineering 3 16%
Mathematics 2 11%
Business, Management and Accounting 2 11%
Arts and Humanities 1 5%
Environmental Science 1 5%
Other 5 26%
Unknown 5 26%
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 06 September 2016.
All research outputs
#17,285,036
of 25,373,627 outputs
Outputs from Computational Statistics & Data Analysis
#492
of 803 outputs
Outputs of similar age
#165,909
of 276,788 outputs
Outputs of similar age from Computational Statistics & Data Analysis
#5
of 7 outputs
Altmetric has tracked 25,373,627 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 803 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 24th percentile – i.e., 24% 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 276,788 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.