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Avoiding and Identifying Errors and Other Threats to the Credibility of Health Economic Models

Overview of attention for article published in PharmacoEconomics, July 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 (86th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

policy
1 policy source
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11 X users
facebook
1 Facebook page

Citations

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18 Dimensions

Readers on

mendeley
44 Mendeley
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1 CiteULike
Title
Avoiding and Identifying Errors and Other Threats to the Credibility of Health Economic Models
Published in
PharmacoEconomics, July 2014
DOI 10.1007/s40273-014-0186-2
Pubmed ID
Authors

Paul Tappenden, James B. Chilcott

Abstract

Health economic models have become the primary vehicle for undertaking economic evaluation and are used in various healthcare jurisdictions across the world to inform decisions about the use of new and existing health technologies. Models are required because a single source of evidence, such as a randomised controlled trial, is rarely sufficient to provide all relevant information about the expected costs and health consequences of all competing decision alternatives. Whilst models are used to synthesise all relevant evidence, they also contain assumptions, abstractions and simplifications. By their very nature, all models are therefore 'wrong'. As such, the interpretation of estimates of the cost effectiveness of health technologies requires careful judgements about the degree of confidence that can be placed in the models from which they are drawn. The presence of a single error or inappropriate judgement within a model may lead to inappropriate decisions, an inefficient allocation of healthcare resources and ultimately suboptimal outcomes for patients. This paper sets out a taxonomy of threats to the credibility of health economic models. The taxonomy segregates threats to model credibility into three broad categories: (i) unequivocal errors, (ii) violations, and (iii) matters of judgement; and maps these across the main elements of the model development process. These three categories are defined according to the existence of criteria for judging correctness, the degree of force with which such criteria can be applied, and the means by which these credibility threats can be handled. A range of suggested processes and techniques for avoiding and identifying these threats is put forward with the intention of prospectively improving the credibility of models.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 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 %
United Kingdom 2 5%
Switzerland 1 2%
Unknown 41 93%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 20%
Researcher 7 16%
Student > Ph. D. Student 6 14%
Student > Bachelor 2 5%
Student > Doctoral Student 2 5%
Other 7 16%
Unknown 11 25%
Readers by discipline Count As %
Medicine and Dentistry 11 25%
Economics, Econometrics and Finance 8 18%
Pharmacology, Toxicology and Pharmaceutical Science 2 5%
Business, Management and Accounting 2 5%
Nursing and Health Professions 2 5%
Other 7 16%
Unknown 12 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 21 June 2019.
All research outputs
#2,936,445
of 23,314,015 outputs
Outputs from PharmacoEconomics
#283
of 1,875 outputs
Outputs of similar age
#30,016
of 228,161 outputs
Outputs of similar age from PharmacoEconomics
#5
of 34 outputs
Altmetric has tracked 23,314,015 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,875 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has done well, scoring higher than 84% 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 228,161 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 86% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.