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Systematic narrative review of decision frameworks to select the appropriate modelling approaches for health economic evaluations

Overview of attention for article published in BMC Research Notes, June 2015
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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Title
Systematic narrative review of decision frameworks to select the appropriate modelling approaches for health economic evaluations
Published in
BMC Research Notes, June 2015
DOI 10.1186/s13104-015-1202-0
Pubmed ID
Authors

B Tsoi, D O’Reilly, J Jegathisawaran, J-E Tarride, G Blackhouse, R Goeree

Abstract

In constructing or appraising a health economic model, an early consideration is whether the modelling approach selected is appropriate for the given decision problem. Frameworks and taxonomies that distinguish between modelling approaches can help make this decision more systematic and this study aims to identify and compare the decision frameworks proposed to date on this topic area. A systematic review was conducted to identify frameworks from peer-reviewed and grey literature sources. The following databases were searched: OVID Medline and EMBASE; Wiley's Cochrane Library and Health Economic Evaluation Database; PubMed; and ProQuest. Eight decision frameworks were identified, each focused on a different set of modelling approaches and employing a different collection of selection criterion. The selection criteria can be categorized as either: (i) structural features (i.e. technical elements that are factual in nature) or (ii) practical considerations (i.e. context-dependent attributes). The most commonly mentioned structural features were population resolution (i.e. aggregate vs. individual) and interactivity (i.e. static vs. dynamic). Furthermore, understanding the needs of the end-users and stakeholders was frequently incorporated as a criterion within these frameworks. There is presently no universally-accepted framework for selecting an economic modelling approach. Rather, each highlights different criteria that may be of importance when determining whether a modelling approach is appropriate. Further discussion is thus necessary as the modelling approach selected will impact the validity of the underlying economic model and have downstream implications on its efficiency, transparency and relevance to decision-makers.

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X Demographics

The data shown below were collected from the profiles of 4 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 21%
Researcher 5 15%
Student > Postgraduate 3 9%
Student > Master 2 6%
Student > Bachelor 1 3%
Other 4 12%
Unknown 12 35%
Readers by discipline Count As %
Medicine and Dentistry 8 24%
Nursing and Health Professions 2 6%
Business, Management and Accounting 2 6%
Environmental Science 2 6%
Economics, Econometrics and Finance 2 6%
Other 4 12%
Unknown 14 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 23 February 2016.
All research outputs
#13,440,839
of 22,815,414 outputs
Outputs from BMC Research Notes
#1,682
of 4,262 outputs
Outputs of similar age
#124,298
of 264,340 outputs
Outputs of similar age from BMC Research Notes
#27
of 79 outputs
Altmetric has tracked 22,815,414 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,262 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 58% 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 264,340 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 51% of its contemporaries.
We're also able to compare this research output to 79 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 62% of its contemporaries.