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External validation of a Cox prognostic model: principles and methods

Overview of attention for article published in BMC Medical Research Methodology, March 2013
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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 (91st percentile)

Mentioned by

news
1 news outlet
twitter
11 tweeters

Citations

dimensions_citation
278 Dimensions

Readers on

mendeley
335 Mendeley
citeulike
3 CiteULike
Title
External validation of a Cox prognostic model: principles and methods
Published in
BMC Medical Research Methodology, March 2013
DOI 10.1186/1471-2288-13-33
Pubmed ID
Authors

Patrick Royston, Douglas G Altman

Abstract

A prognostic model should not enter clinical practice unless it has been demonstrated that it performs a useful role. External validation denotes evaluation of model performance in a sample independent of that used to develop the model. Unlike for logistic regression models, external validation of Cox models is sparsely treated in the literature. Successful validation of a model means achieving satisfactory discrimination and calibration (prediction accuracy) in the validation sample. Validating Cox models is not straightforward because event probabilities are estimated relative to an unspecified baseline function.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 335 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 2%
United Kingdom 4 1%
Germany 2 <1%
Italy 2 <1%
Australia 2 <1%
Canada 1 <1%
Netherlands 1 <1%
Vietnam 1 <1%
Iceland 1 <1%
Other 3 <1%
Unknown 312 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 86 26%
Student > Ph. D. Student 74 22%
Other 33 10%
Student > Master 29 9%
Professor > Associate Professor 25 7%
Other 58 17%
Unknown 30 9%
Readers by discipline Count As %
Medicine and Dentistry 143 43%
Mathematics 35 10%
Agricultural and Biological Sciences 21 6%
Computer Science 14 4%
Biochemistry, Genetics and Molecular Biology 7 2%
Other 51 15%
Unknown 64 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 26 October 2019.
All research outputs
#1,080,438
of 14,185,873 outputs
Outputs from BMC Medical Research Methodology
#164
of 1,303 outputs
Outputs of similar age
#12,038
of 146,890 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 1 outputs
Altmetric has tracked 14,185,873 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,303 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.3. This one has done well, scoring higher than 87% 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 146,890 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 91% of its contemporaries.
We're also able to compare this research output to 1 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