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Design of early validation trials of biomarkers.

Normolle D, Ruffin MT, Brenner D - Cancer Inform (2005)

Bottom Line: Early-phase studies must be designed as part of a development program, considering the final use of the marker, directly informing the decision to made at the study's conclusion.Therefore, they should test for sensitivity and specificity that would be minimally acceptable to proceed to the next stage of development.Receiver operating characteristic (ROC) curves, which are useful descriptive tools, may be misleading when evaluating tests in low-prevalence populations, because they emphasize the relationship between specificity and sensitivity in the range of specificity likely to be too low to be useful in mass screening applications.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiation Oncology, University of Michigan Medical School, University of Michigan Comprehensive Cancer Center Biostatistics Unit, USA. monk@umich.edu

ABSTRACT
The design of early-phase studies of putative screening markers in clinical populations is discussed. Biological, epidemiological, statistical and computational issues all affect the design of early-phase studies of these markers, but there are frequently little or no data in hand to facilitate the design. Early-phase studies must be designed as part of a development program, considering the final use of the marker, directly informing the decision to made at the study's conclusion. Therefore, they should test for sensitivity and specificity that would be minimally acceptable to proceed to the next stage of development. Designing such trials requires explicit assumptions about prevalence and false positive and negative costs in the ultimate target population. Early discussion of these issues strengthens the development process, since enthusiasm for developing technologies is balanced by realism about the requirements of a valid population screen. Receiver operating characteristic (ROC) curves, which are useful descriptive tools, may be misleading when evaluating tests in low-prevalence populations, because they emphasize the relationship between specificity and sensitivity in the range of specificity likely to be too low to be useful in mass screening applications.

No MeSH data available.


ROC curve for a normally-distributed univariate random variable. The sensitivity and specificity values that minimize the expected classification cost for the given prevalence and C−/C+ values is displayed.
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f1-cin-01-25: ROC curve for a normally-distributed univariate random variable. The sensitivity and specificity values that minimize the expected classification cost for the given prevalence and C−/C+ values is displayed.

Mentions: That is, given an ROC curve, the optimal combination of sensitivity and specificity are determined by the point on the curve with the slope as described in Equation 2. The application of the optimal rule to the preliminary data generated by the colon-cancer screening example, assuming a prevalence of 300/100,000 and a C−/C+ ratio of 100, is demonstrated in Figure 1.


Design of early validation trials of biomarkers.

Normolle D, Ruffin MT, Brenner D - Cancer Inform (2005)

ROC curve for a normally-distributed univariate random variable. The sensitivity and specificity values that minimize the expected classification cost for the given prevalence and C−/C+ values is displayed.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC2657653&req=5

f1-cin-01-25: ROC curve for a normally-distributed univariate random variable. The sensitivity and specificity values that minimize the expected classification cost for the given prevalence and C−/C+ values is displayed.
Mentions: That is, given an ROC curve, the optimal combination of sensitivity and specificity are determined by the point on the curve with the slope as described in Equation 2. The application of the optimal rule to the preliminary data generated by the colon-cancer screening example, assuming a prevalence of 300/100,000 and a C−/C+ ratio of 100, is demonstrated in Figure 1.

Bottom Line: Early-phase studies must be designed as part of a development program, considering the final use of the marker, directly informing the decision to made at the study's conclusion.Therefore, they should test for sensitivity and specificity that would be minimally acceptable to proceed to the next stage of development.Receiver operating characteristic (ROC) curves, which are useful descriptive tools, may be misleading when evaluating tests in low-prevalence populations, because they emphasize the relationship between specificity and sensitivity in the range of specificity likely to be too low to be useful in mass screening applications.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiation Oncology, University of Michigan Medical School, University of Michigan Comprehensive Cancer Center Biostatistics Unit, USA. monk@umich.edu

ABSTRACT
The design of early-phase studies of putative screening markers in clinical populations is discussed. Biological, epidemiological, statistical and computational issues all affect the design of early-phase studies of these markers, but there are frequently little or no data in hand to facilitate the design. Early-phase studies must be designed as part of a development program, considering the final use of the marker, directly informing the decision to made at the study's conclusion. Therefore, they should test for sensitivity and specificity that would be minimally acceptable to proceed to the next stage of development. Designing such trials requires explicit assumptions about prevalence and false positive and negative costs in the ultimate target population. Early discussion of these issues strengthens the development process, since enthusiasm for developing technologies is balanced by realism about the requirements of a valid population screen. Receiver operating characteristic (ROC) curves, which are useful descriptive tools, may be misleading when evaluating tests in low-prevalence populations, because they emphasize the relationship between specificity and sensitivity in the range of specificity likely to be too low to be useful in mass screening applications.

No MeSH data available.