Biomedical subjects

Nancy A Obuchowski

Publications and source records attributed to Nancy A Obuchowski.

21 records · Page 2Linked to original sources

PubMed2003

Receiver operating characteristic curves and their use in radiology.

Sensitivity and specificity are the basic measures of accuracy of a diagnostic test; however, they depend on the cut point used to define "positive" and "negative" test results. As the cut point shifts, sensitivity and specificity shift. The receiver operating characteristic (ROC) curve is a plot of the sensitivity of a test versus its false-positive rate for all possible cut points. The advantages of the ROC curve as a means of defining the accuracy of a test, construction of the ROC, and identification of the optimal cut point on the ROC curve are discussed. Several summary measures of the accuracy of a test, including the commonly used percentage of correct diagnoses and area under the ROC curve, are described and compared. Two examples of ROC curve application in radiologic research are presented.

Area Under Curve↗

PubMed2002

Confidence bounds when the estimated ROC area is 1.01.

RATIONALE AND OBJECTIVES: In studies with small samples, the authors often encounter data sets in which the estimated area under the receiver operating characteristic (ROC) curve is 1.0. In such cases, neither asymptotic nor resampling methods provide a means of estimating the standard error or constructing a lower confidence bound. The purpose of this study was to develop tables for determining the approximate 95% lower confidence bound when the estimated ROC area is 1.0. MATERIALS AND METHODS: Using Monte Carlo simulation, the authors generated 10,000 data sets for each specification of sample sizes, ROC curve shape, and data format (continuous and ordinal scale). For each of these combinations the authors determined the 95% lower confidence bound. RESULTS: When the estimated ROC area is 1.0, the 95% lower confidence bounds differ dramatically depending on the shape of the ROC curve and on whether the test results are ordinal or continuous. Four tables of 95% lower confidence bounds are provided, along with guidelines for their use. CONCLUSION: Given the different shapes of ROC curves and the different formats in which ROC data are collected, it is not feasible to offer one simple method of constructing confidence bounds that works for all ROC curves. The tables provided in this article are useful for interpreting studies with estimated ROC areas of 1.0.

Area Under Curve↗

PubMed2002

Prospective studies of diagnostic test accuracy when disease prevalence is low.

Prospective studies of diagnostic test accuracy have important advantages over retrospective designs. Yet, when the disease being detected by the diagnostic test(s) has a low prevalence rate, a prospective design can require an enormous sample of patients. We consider two strategies to reduce the costs of prospective studies of binary diagnostic tests: stratification and two-phase sampling. Utilizing neither, one, or both of these strategies provides us with four study design options: (1) the conventional design involving a simple random sample (SRS) of patients from the clinical population; (2) a stratified design where patients from higher-prevalence subpopulations are more heavily sampled; (3) a simple two-phase design using a SRS in the first phase and selection for the second phase based on the test results from the first; and (4) a two-phase design with stratification in the first phase. We describe estimators for sensitivity and specificity and their variances for each design, along with sample size estimation. We offer some recommendations for choosing among the various designs. We illustrate the study designs with two examples.

Journal Article↗