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Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration

Overview of attention for article published in BMC Medicine, October 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

news
1 news outlet
policy
1 policy source
twitter
18 X users
patent
1 patent

Citations

dimensions_citation
111 Dimensions

Readers on

mendeley
146 Mendeley
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1 CiteULike
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Title
Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration
Published in
BMC Medicine, October 2013
DOI 10.1186/1741-7015-11-220
Pubmed ID
Authors

Lisa M McShane, Margaret M Cavenagh, Tracy G Lively, David A Eberhard, William L Bigbee, P Mickey Williams, Jill P Mesirov, Mei-Yin C Polley, Kelly Y Kim, James V Tricoli, Jeremy MG Taylor, Deborah J Shuman, Richard M Simon, James H Doroshow, Barbara A Conley

Abstract

High-throughput 'omics' technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.

X Demographics

X Demographics

The data shown below were collected from the profiles of 18 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 146 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Denmark 1 <1%
France 1 <1%
Germany 1 <1%
Unknown 142 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 44 30%
Student > Ph. D. Student 22 15%
Student > Master 12 8%
Other 11 8%
Student > Bachelor 10 7%
Other 15 10%
Unknown 32 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 23%
Medicine and Dentistry 32 22%
Biochemistry, Genetics and Molecular Biology 15 10%
Engineering 11 8%
Computer Science 4 3%
Other 19 13%
Unknown 32 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 25. 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 March 2023.
All research outputs
#1,542,714
of 25,773,273 outputs
Outputs from BMC Medicine
#1,089
of 4,091 outputs
Outputs of similar age
#13,874
of 225,551 outputs
Outputs of similar age from BMC Medicine
#27
of 59 outputs
Altmetric has tracked 25,773,273 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,091 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 46.2. This one has gotten more attention than average, scoring higher than 73% 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 225,551 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 59 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 54% of its contemporaries.