Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration
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Mc Shane, Lisa M, et al. Criteria for the Use of Omics-based Predictors In Clinical Trials: Explanation and Elaboration. BioMed Central, 2013. https://doi.org/10.17615/jmn7-ve25APA
Mc Shane, L., Cavenagh, M., Lively, T., Eberhard, D., Bigbee, W., Williams, P., Mesirov, J., Polley, M., Kim, K., Tricoli, J., Taylor, J., Shuman, D., Simon, R., Doroshow, J., & Conley, B. (2013). Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration. BioMed Central. https://doi.org/10.17615/jmn7-ve25Chicago
Mc Shane, Lisa M, Margaret M Cavenagh, Tracy G Lively, David Eberhard, William L Bigbee, P Mickey Williams, Jill P Mesirov et al. 2013. Criteria for the Use of Omics-Based Predictors In Clinical Trials: Explanation and Elaboration. BioMed Central. https://doi.org/10.17615/jmn7-ve25- Creator
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McShane, Lisa M
- Other Affiliation: Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health
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Cavenagh, Margaret M
- Other Affiliation: Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health
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Lively, Tracy G
- Other Affiliation: Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health
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Eberhard, David
- Affiliation: School of Medicine, Department of Pathology and Laboratory Medicine
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Bigbee, William L
- Other Affiliation: Department of Pathology and University of Pittsburgh Cancer Institute
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Williams, P Mickey
- Other Affiliation: Frederick National Laboratory for Cancer Research, National Cancer Institute, National Institutes of Health
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Mesirov, Jill P
- Other Affiliation: Computational Biology and Bioinformatics, Broad Institute of Massachusetts Institute of Technology and Harvard University
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Polley, Mei-Yin C
- Other Affiliation: Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health
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Kim, Kelly Y
- Other Affiliation: Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health
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Tricoli, James V
- Other Affiliation: Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health
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Taylor, Jeremy M
- Other Affiliation: Department of Biostatistics, University of Michigan
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Shuman, Deborah J
- Other Affiliation: Office of the Director, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health
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Simon, Richard M
- Other Affiliation: Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health
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Doroshow, James H
- Other Affiliation: Office of the Director, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health
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Conley, Barbara A
- Other Affiliation: Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health
- Abstract
- 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.
- Date of publication
- October 17, 2013
- DOI
- Identifier
- Resource type
- Journal Item
- Rights statement
- In Copyright
- Rights holder
- McShane et al.; licensee BioMed Central Ltd.
- Language
- English
- Bibliographic citation
- BMC Medicine. 2013 Oct 17;11(1):220
- Publisher
- BioMed Central
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