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Planning clinically relevant biomarker validation studies using the “number needed to treat” concept

Overview of attention for article published in Journal of Translational Medicine, May 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

twitter
3 tweeters

Readers on

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30 Mendeley
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Title
Planning clinically relevant biomarker validation studies using the “number needed to treat” concept
Published in
Journal of Translational Medicine, May 2016
DOI 10.1186/s12967-016-0862-4
Pubmed ID
Authors

Roger S. Day

Abstract

Despite an explosion of translational research to exploit biomarkers in diagnosis, prediction and prognosis, the impact of biomarkers on clinical practice has been limited. The elusiveness of clinical utility may partly originate when validation studies are planned, from a failure to articulate precisely how the biomarker, if successful, will improve clinical decision-making for patients. Clarifying what performance would suffice if the test is to improve medical care makes it possible to design meaningful validation studies. But methods for tackling this part of validation study design are undeveloped, because it demands uncomfortable judgments about the relative values of good and bad outcomes resulting from a medical decision. An unconventional use of "number needed to treat" (NNT) can structure communication for the trial design team, to elicit purely value-based outcome tradeoffs, conveyed as the endpoints of an NNT "discomfort range". The study biostatistician can convert the endpoints into desired predictive values, providing criteria for designing a prospective validation study. Next, a novel "contra-Bayes" theorem converts those predictive values into target sensitivity and specificity criteria, to guide design of a retrospective validation study. Several examples demonstrate the approach. In practice, NNT-guided dialogues have contributed to validation study planning by tying it closely to specific patient-oriented translational goals. The ultimate payoff comes when the report of the completed study includes motivation in the form of a biomarker test framework directly reflecting the clinical decision challenge to be solved. Then readers will understand better what the biomarker test has to offer patients.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 29 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 23%
Student > Bachelor 4 13%
Professor > Associate Professor 3 10%
Professor 2 7%
Student > Master 2 7%
Other 6 20%
Unknown 6 20%
Readers by discipline Count As %
Medicine and Dentistry 5 17%
Agricultural and Biological Sciences 5 17%
Biochemistry, Genetics and Molecular Biology 3 10%
Nursing and Health Professions 2 7%
Computer Science 1 3%
Other 7 23%
Unknown 7 23%

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 21 October 2017.
All research outputs
#6,764,283
of 12,022,940 outputs
Outputs from Journal of Translational Medicine
#995
of 2,331 outputs
Outputs of similar age
#123,911
of 276,116 outputs
Outputs of similar age from Journal of Translational Medicine
#42
of 98 outputs
Altmetric has tracked 12,022,940 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,331 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has gotten more attention than average, scoring higher than 54% 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 276,116 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 53% of its contemporaries.
We're also able to compare this research output to 98 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 55% of its contemporaries.