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Patient Variability Seldom Assessed in Cost-effectiveness Studies

Overview of attention for article published in Medical Decision Making, January 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

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31 tweeters

Citations

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9 Dimensions

Readers on

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23 Mendeley
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Title
Patient Variability Seldom Assessed in Cost-effectiveness Studies
Published in
Medical Decision Making, January 2018
DOI 10.1177/0272989x17746989
Pubmed ID
Authors

Tara A. Lavelle, David M. Kent, Christine M. Lundquist, Teja Thorat, Joshua T. Cohen, John B. Wong, Natalia Olchanski, Peter J. Neumann

Abstract

Cost-effectiveness analysis (CEA) estimates can vary substantially across patient subgroups when patient characteristics influence preferences, outcome risks, treatment effectiveness, life expectancy, or associated costs. However, no systematic review has reported the frequency of subgroup analysis in CEA, what type of heterogeneity they address, and how often heterogeneity influences whether cost-effectiveness ratios exceed or fall below conventional thresholds. We reviewed the CEA literature cataloged in the Tufts Medical Center CEA Registry, a repository describing cost-utility analyses published through 2016. After randomly selecting 200 of 642 articles published in 2014, we ascertained whether each study reported subgroup results and collected data on the defining characteristics of these subgroups. We identified whether any of the CEA subgroup results crossed conventional cost-effectiveness benchmarks (e.g., $100,000 per QALY) and compared characteristics of studies with and without subgroup-specific findings. Thirty-eight studies (19%) reported patient subgroup results. Articles reporting subgroup analyses were more likely to be US-based, government funded (v. drug industry- or nonprofit foundation-funded) studies, with a focus on primary or secondary (v. tertiary) prevention (P < 0.05 for comparisons). One or more patient characteristics were used to stratify CEA results 68 times within the 38 studies, with most stratifications using one characteristic (n = 47), most commonly age (n = 35). Among the 23 stratifications reported alongside average ratios in US studies, 13 produced subgroup ratios that crossed a conventional CEA ratio benchmark. Most CEAs do not report any subgroup results, and those that do most often stratify only by patient age. Over half of the subgroup analyses reported could lead to different value-based decision making for at least some patients.

Twitter Demographics

The data shown below were collected from the profiles of 31 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 26%
Researcher 5 22%
Student > Bachelor 3 13%
Professor > Associate Professor 2 9%
Student > Master 1 4%
Other 1 4%
Unknown 5 22%
Readers by discipline Count As %
Medicine and Dentistry 7 30%
Engineering 2 9%
Nursing and Health Professions 2 9%
Economics, Econometrics and Finance 1 4%
Psychology 1 4%
Other 3 13%
Unknown 7 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 November 2019.
All research outputs
#1,142,308
of 16,250,537 outputs
Outputs from Medical Decision Making
#92
of 1,128 outputs
Outputs of similar age
#37,843
of 370,418 outputs
Outputs of similar age from Medical Decision Making
#5
of 18 outputs
Altmetric has tracked 16,250,537 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,128 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has done particularly well, scoring higher than 91% 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 370,418 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 89% of its contemporaries.
We're also able to compare this research output to 18 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 72% of its contemporaries.