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Improved curve fits to summary survival data: application to economic evaluation of health technologies

Overview of attention for article published in BMC Medical Research Methodology, October 2011
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)

Mentioned by

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1 policy source
twitter
1 tweeter

Citations

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97 Dimensions

Readers on

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125 Mendeley
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Title
Improved curve fits to summary survival data: application to economic evaluation of health technologies
Published in
BMC Medical Research Methodology, October 2011
DOI 10.1186/1471-2288-11-139
Pubmed ID
Authors

Martin W Hoyle, William Henley

Abstract

Mean costs and quality-adjusted-life-years are central to the cost-effectiveness of health technologies. They are often calculated from time to event curves such as for overall survival and progression-free survival. Ideally, estimates should be obtained from fitting an appropriate parametric model to individual patient data. However, such data are usually not available to independent researchers. Instead, it is common to fit curves to summary Kaplan-Meier graphs, either by regression or by least squares. Here, a more accurate method of fitting survival curves to summary survival data is described.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 2%
Spain 2 2%
United States 2 2%
Unknown 119 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 23%
Student > Master 25 20%
Student > Ph. D. Student 22 18%
Student > Bachelor 14 11%
Other 7 6%
Other 16 13%
Unknown 12 10%
Readers by discipline Count As %
Medicine and Dentistry 40 32%
Pharmacology, Toxicology and Pharmaceutical Science 15 12%
Economics, Econometrics and Finance 12 10%
Nursing and Health Professions 8 6%
Agricultural and Biological Sciences 6 5%
Other 22 18%
Unknown 22 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 February 2018.
All research outputs
#3,158,242
of 12,470,921 outputs
Outputs from BMC Medical Research Methodology
#459
of 1,106 outputs
Outputs of similar age
#24,470
of 95,946 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 1 outputs
Altmetric has tracked 12,470,921 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,106 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. This one has gotten more attention than average, scoring higher than 57% 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 95,946 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% 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