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Recovering the raw data behind a non-parametric survival curve

Overview of attention for article published in Systematic Reviews, December 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

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22 X users

Citations

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40 Dimensions

Readers on

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44 Mendeley
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1 CiteULike
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Title
Recovering the raw data behind a non-parametric survival curve
Published in
Systematic Reviews, December 2014
DOI 10.1186/2046-4053-3-151
Pubmed ID
Authors

Zhihui Liu, Benjamin Rich, James A Hanley

Abstract

Researchers often wish to carry out additional calculations or analyses using the survival data from one or more studies of other authors. When it is not possible to obtain the raw data directly, reconstruction techniques provide a valuable alternative. Several authors have proposed methods/tools for extracting data from such curves using a digitizing software. Instead of using a digitizer to read in the coordinates from a raster image, we propose directly reading in the lines of the PostScript file of a vector image.

X Demographics

X Demographics

The data shown below were collected from the profiles of 22 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 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 1 2%
Unknown 43 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 20%
Student > Ph. D. Student 7 16%
Student > Bachelor 5 11%
Student > Master 4 9%
Student > Postgraduate 3 7%
Other 8 18%
Unknown 8 18%
Readers by discipline Count As %
Medicine and Dentistry 13 30%
Agricultural and Biological Sciences 7 16%
Pharmacology, Toxicology and Pharmaceutical Science 3 7%
Mathematics 3 7%
Environmental Science 1 2%
Other 4 9%
Unknown 13 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 09 September 2022.
All research outputs
#2,892,040
of 25,027,753 outputs
Outputs from Systematic Reviews
#509
of 2,182 outputs
Outputs of similar age
#39,052
of 364,550 outputs
Outputs of similar age from Systematic Reviews
#13
of 44 outputs
Altmetric has tracked 25,027,753 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,182 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.2. This one has done well, scoring higher than 76% 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 364,550 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 89% of its contemporaries.
We're also able to compare this research output to 44 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 70% of its contemporaries.