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Expert Elicitation of Multinomial Probabilities for Decision-Analytic Modeling: An Application to Rates of Disease Progression in Undiagnosed and Untreated Melanoma

Overview of attention for article published in Value in Health (Elsevier Science), June 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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9 tweeters
facebook
1 Facebook page
reddit
1 Redditor

Citations

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3 Dimensions

Readers on

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11 Mendeley
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Title
Expert Elicitation of Multinomial Probabilities for Decision-Analytic Modeling: An Application to Rates of Disease Progression in Undiagnosed and Untreated Melanoma
Published in
Value in Health (Elsevier Science), June 2018
DOI 10.1016/j.jval.2017.10.009
Pubmed ID
Authors

Edward C.F. Wilson, Juliet A. Usher-Smith, Jon Emery, Pippa G. Corrie, Fiona M. Walter

Abstract

Expert elicitation is required to inform decision making when relevant "better quality" data either do not exist or cannot be collected. An example of this is to inform decisions as to whether to screen for melanoma. A key input is the counterfactual, in this case the natural history of melanoma in patients who are undiagnosed and hence untreated. To elicit expert opinion on the probability of disease progression in patients with melanoma that is undetected and hence untreated. A bespoke webinar-based expert elicitation protocol was administered to 14 participants in the United Kingdom, Australia, and New Zealand, comprising 12 multinomial questions on the probability of progression from one disease stage to another in the absence of treatment. A modified Connor-Mosimann distribution was fitted to individual responses to each question. Individual responses were pooled using a Monte-Carlo simulation approach. Participants were asked to provide feedback on the process. A pooled modified Connor-Mosimann distribution was successfully derived from participants' responses. Feedback from participants was generally positive, with 86% willing to take part in such an exercise again. Nevertheless, only 57% of participants felt that this was a valid approach to determine the risk of disease progression. Qualitative feedback reflected some understanding of the need to rely on expert elicitation in the absence of "hard" data. We successfully elicited and pooled the beliefs of experts in melanoma regarding the probability of disease progression in a format suitable for inclusion in a decision-analytic model.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 45%
Student > Master 1 9%
Student > Bachelor 1 9%
Student > Postgraduate 1 9%
Other 1 9%
Other 2 18%
Readers by discipline Count As %
Unspecified 3 27%
Nursing and Health Professions 2 18%
Computer Science 1 9%
Business, Management and Accounting 1 9%
Agricultural and Biological Sciences 1 9%
Other 3 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 20 June 2019.
All research outputs
#3,025,982
of 13,532,334 outputs
Outputs from Value in Health (Elsevier Science)
#445
of 2,547 outputs
Outputs of similar age
#98,086
of 388,833 outputs
Outputs of similar age from Value in Health (Elsevier Science)
#17
of 51 outputs
Altmetric has tracked 13,532,334 research outputs across all sources so far. Compared to these this one has done well and is in the 77th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,547 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 82% 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 388,833 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 74% of its contemporaries.
We're also able to compare this research output to 51 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 66% of its contemporaries.