Early-Phase Studies of Biomarkers: What Target Sensitivity and Specificity Values Might Confer Clinical Utility?
Many cancer biomarker research studies seek to develop markers that can accurately detect or predict future onset of disease. To design and evaluate these studies, one must specify the levels of accuracy sought. However, justified target levels are rarely available. We describe a way to calculate ta...
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| Vydané v: | Clinical chemistry (Baltimore, Md.) Ročník 62; číslo 5; s. 737 - 742 |
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| Médium: | Journal Article |
| Jazyk: | English |
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England
Oxford University Press
01.05.2016
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| ISSN: | 0009-9147, 1530-8561 |
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| Abstract | Many cancer biomarker research studies seek to develop markers that can accurately detect or predict future onset of disease. To design and evaluate these studies, one must specify the levels of accuracy sought. However, justified target levels are rarely available.
We describe a way to calculate target levels of sensitivity and specificity for a biomarker intended to be applied in a defined clinical context. The calculation requires knowledge of the prevalence or incidence of cases in the clinical population and the ratio of benefit associated with the clinical consequences of a positive biomarker test in cases (true positive) to cost associated with a positive biomarker test in controls (false positive). Guidance is offered on soliciting the cost/benefit ratio. The calculations are based on the longstanding decision theory concept of providing a net benefit on average in the population, and they rely on some assumptions about uniformity of costs and benefits to those tested.
Calculations are illustrated with 3 applications: predicting colon cancer recurrence in stage 1 patients; predicting interval breast cancer (between mammography screenings); and screening for ovarian cancer.
It is feasible to specify target levels of biomarker performance that enable evaluation of the potential clinical impact of biomarkers in early-phase studies. Nevertheless, biomarkers meeting the criteria should still be tested rigorously in studies that measure the actual impact on patient outcomes of using the biomarker to make clinical decisions. |
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| AbstractList | Many cancer biomarker research studies seek to develop markers that can accurately detect or predict future onset of disease. To design and evaluate these studies, one must specify the levels of accuracy sought. However, justified target levels are rarely available.
We describe a way to calculate target levels of sensitivity and specificity for a biomarker intended to be applied in a defined clinical context. The calculation requires knowledge of the prevalence or incidence of cases in the clinical population and the ratio of benefit associated with the clinical consequences of a positive biomarker test in cases (true positive) to cost associated with a positive biomarker test in controls (false positive). Guidance is offered on soliciting the cost/benefit ratio. The calculations are based on the longstanding decision theory concept of providing a net benefit on average in the population, and they rely on some assumptions about uniformity of costs and benefits to those tested.
Calculations are illustrated with 3 applications: predicting colon cancer recurrence in stage 1 patients; predicting interval breast cancer (between mammography screenings); and screening for ovarian cancer.
It is feasible to specify target levels of biomarker performance that enable evaluation of the potential clinical impact of biomarkers in early-phase studies. Nevertheless, biomarkers meeting the criteria should still be tested rigorously in studies that measure the actual impact on patient outcomes of using the biomarker to make clinical decisions. BACKGROUNDMany cancer biomarker research studies seek to develop markers that can accurately detect or predict future onset of disease. To design and evaluate these studies, one must specify the levels of accuracy sought. However, justified target levels are rarely available.METHODSWe describe a way to calculate target levels of sensitivity and specificity for a biomarker intended to be applied in a defined clinical context. The calculation requires knowledge of the prevalence or incidence of cases in the clinical population and the ratio of benefit associated with the clinical consequences of a positive biomarker test in cases (true positive) to cost associated with a positive biomarker test in controls (false positive). Guidance is offered on soliciting the cost/benefit ratio. The calculations are based on the longstanding decision theory concept of providing a net benefit on average in the population, and they rely on some assumptions about uniformity of costs and benefits to those tested.RESULTSCalculations are illustrated with 3 applications: predicting colon cancer recurrence in stage 1 patients; predicting interval breast cancer (between mammography screenings); and screening for ovarian cancer.CONCLUSIONSIt is feasible to specify target levels of biomarker performance that enable evaluation of the potential clinical impact of biomarkers in early-phase studies. Nevertheless, biomarkers meeting the criteria should still be tested rigorously in studies that measure the actual impact on patient outcomes of using the biomarker to make clinical decisions. Many cancer biomarker research studies seek to develop markers that can accurately detect or predict future onset of disease. To design and evaluate these studies, one must specify the levels of accuracy sought. However, justified target levels are rarely available. We describe a way to calculate target levels of sensitivity and specificity for a biomarker intended to be applied in a defined clinical context. The calculation requires knowledge of the prevalence or incidence of cases in the clinical population and the ratio of benefit associated with the clinical consequences of a positive biomarker test in cases (true positive) to cost associated with a positive biomarker test in controls (false positive). Guidance is offered on soliciting the cost/benefit ratio. The calculations are based on the longstanding decision theory concept of providing a net benefit on average in the population, and they rely on some assumptions about uniformity of costs and benefits to those tested. Calculations are illustrated with 3 applications: predicting colon cancer recurrence in stage 1 patients; predicting interval breast cancer (between mammography screenings); and screening for ovarian cancer. It is feasible to specify target levels of biomarker performance that enable evaluation of the potential clinical impact of biomarkers in early-phase studies. Nevertheless, biomarkers meeting the criteria should still be tested rigorously in studies that measure the actual impact on patient outcomes of using the biomarker to make clinical decisions. |
| Author | Bossuyt, Patrick M Feng, Ziding Pepe, Margaret S Janes, Holly Hilden, Jørgen Li, Christopher I |
| AuthorAffiliation | 4 Clinical Epidemiology, University of Amsterdam, Amsterdam, Netherlands 2 Vaccine and Infectious Disease Division, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 6 University of Copenhagen, Institute of Medical Genetics, Dept. of Biostatistics, Copenhagen, Denmark 3 Translational Research Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 1 Biostatistics and Biomathematics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 5 Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas |
| AuthorAffiliation_xml | – name: 3 Translational Research Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington – name: 2 Vaccine and Infectious Disease Division, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington – name: 1 Biostatistics and Biomathematics Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington – name: 5 Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas – name: 4 Clinical Epidemiology, University of Amsterdam, Amsterdam, Netherlands – name: 6 University of Copenhagen, Institute of Medical Genetics, Dept. of Biostatistics, Copenhagen, Denmark |
| Author_xml | – sequence: 1 givenname: Margaret S surname: Pepe fullname: Pepe, Margaret S organization: Biostatistics and Biomathematics Program, Public Health Sciences Division – sequence: 2 givenname: Holly surname: Janes fullname: Janes, Holly organization: Vaccine and Infectious Disease Division, Public Health Sciences Division, and – sequence: 3 givenname: Christopher I surname: Li fullname: Li, Christopher I organization: Translational Research Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA – sequence: 4 givenname: Patrick M surname: Bossuyt fullname: Bossuyt, Patrick M organization: Clinical Epidemiology, University of Amsterdam, Amsterdam, Netherlands – sequence: 5 givenname: Ziding surname: Feng fullname: Feng, Ziding organization: Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX – sequence: 6 givenname: Jørgen surname: Hilden fullname: Hilden, Jørgen organization: Department of Biostatistics, Institute of Medical Genetics, University of Copenhagen, Copenhagen, Denmark |
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| Snippet | Many cancer biomarker research studies seek to develop markers that can accurately detect or predict future onset of disease. To design and evaluate these... BACKGROUNDMany cancer biomarker research studies seek to develop markers that can accurately detect or predict future onset of disease. To design and evaluate... |
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| SubjectTerms | Aged Biomarkers Biomarkers, Tumor - analysis Breast Neoplasms - diagnosis Clinical outcomes Colonic Neoplasms - diagnosis Costs Decision theory Design Female Funding Humans Middle Aged Ovarian cancer Ovarian Neoplasms - diagnosis Sensitivity and Specificity |
| Title | Early-Phase Studies of Biomarkers: What Target Sensitivity and Specificity Values Might Confer Clinical Utility? |
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