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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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3 X users

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44 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 States 1 2%
Unknown 43 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 18%
Student > Bachelor 5 11%
Unspecified 5 11%
Student > Ph. D. Student 4 9%
Professor 3 7%
Other 8 18%
Unknown 11 25%
Readers by discipline Count As %
Medicine and Dentistry 8 18%
Agricultural and Biological Sciences 5 11%
Unspecified 5 11%
Biochemistry, Genetics and Molecular Biology 4 9%
Nursing and Health Professions 2 5%
Other 9 20%
Unknown 11 25%
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 21 October 2017.
All research outputs
#14,422,753
of 23,566,295 outputs
Outputs from Journal of Translational Medicine
#1,779
of 4,181 outputs
Outputs of similar age
#156,626
of 300,649 outputs
Outputs of similar age from Journal of Translational Medicine
#47
of 99 outputs
Altmetric has tracked 23,566,295 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,181 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one has gotten more attention than average, scoring higher than 55% 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 300,649 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.