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Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological review of health technology assessments

Overview of attention for article published in BMC Medical Research Methodology, April 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)

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11 tweeters

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10 Dimensions

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50 Mendeley
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Title
Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological review of health technology assessments
Published in
BMC Medical Research Methodology, April 2017
DOI 10.1186/s12874-017-0331-7
Pubmed ID
Authors

Bethany Shinkins, Yaling Yang, Lucy Abel, Thomas R. Fanshawe

Abstract

Evaluations of diagnostic tests are challenging because of the indirect nature of their impact on patient outcomes. Model-based health economic evaluations of tests allow different types of evidence from various sources to be incorporated and enable cost-effectiveness estimates to be made beyond the duration of available study data. To parameterize a health-economic model fully, all the ways a test impacts on patient health must be quantified, including but not limited to diagnostic test accuracy. We assessed all UK NIHR HTA reports published May 2009-July 2015. Reports were included if they evaluated a diagnostic test, included a model-based health economic evaluation and included a systematic review and meta-analysis of test accuracy. From each eligible report we extracted information on the following topics: 1) what evidence aside from test accuracy was searched for and synthesised, 2) which methods were used to synthesise test accuracy evidence and how did the results inform the economic model, 3) how/whether threshold effects were explored, 4) how the potential dependency between multiple tests in a pathway was accounted for, and 5) for evaluations of tests targeted at the primary care setting, how evidence from differing healthcare settings was incorporated. The bivariate or HSROC model was implemented in 20/22 reports that met all inclusion criteria. Test accuracy data for health economic modelling was obtained from meta-analyses completely in four reports, partially in fourteen reports and not at all in four reports. Only 2/7 reports that used a quantitative test gave clear threshold recommendations. All 22 reports explored the effect of uncertainty in accuracy parameters but most of those that used multiple tests did not allow for dependence between test results. 7/22 tests were potentially suitable for primary care but the majority found limited evidence on test accuracy in primary care settings. The uptake of appropriate meta-analysis methods for synthesising evidence on diagnostic test accuracy in UK NIHR HTAs has improved in recent years. Future research should focus on other evidence requirements for cost-effectiveness assessment, threshold effects for quantitative tests and the impact of multiple diagnostic tests.

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 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 49 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 22%
Other 5 10%
Student > Bachelor 5 10%
Student > Master 5 10%
Student > Postgraduate 3 6%
Other 8 16%
Unknown 13 26%
Readers by discipline Count As %
Medicine and Dentistry 14 28%
Pharmacology, Toxicology and Pharmaceutical Science 6 12%
Biochemistry, Genetics and Molecular Biology 3 6%
Computer Science 2 4%
Economics, Econometrics and Finance 2 4%
Other 9 18%
Unknown 14 28%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 13 October 2017.
All research outputs
#2,762,989
of 16,479,073 outputs
Outputs from BMC Medical Research Methodology
#483
of 1,555 outputs
Outputs of similar age
#49,361
of 220,542 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 1 outputs
Altmetric has tracked 16,479,073 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,555 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.8. This one has gotten more attention than average, scoring higher than 68% of its peers.
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