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Sample size tables for receiver operating characteristic studies.

Abstract

OBJECTIVE: I provide researchers with tables of sample size for multiobserver receiver operating characteristic (ROC) studies that compare the diagnostic accuracies of two imaging techniques. MATERIALS AND METHODS: I computed the number of patients and observers needed as a function of five parameters: the measure of diagnostic accuracy (area under the ROC curve, sensitivity at a false-positive rate </= 0.10, or specificity at a false-negative rate </= 0.10), conjectured level of accuracy, suspected difference in accuracy between the two imaging techniques, observer variability, and ratio of patients without to patients with the condition. RESULTS: The numbers of patients and observers required vary dramatically with these five parameters, increasing with more refined measures of accuracy, with lower accuracy levels, with smaller suspected differences, with greater observer variability, and with less balanced designs. The number of patients required for a study can be reduced by increasing the number of observers, and vice versa. When the intra- and interobserver variability is large, a study design with just four observers is usually inadequate. CONCLUSION: Many factors must be considered when determining the appropriate sample sizes for multiobserver ROC studies. My tables serve only as initial ballpark estimates. Investigators should compute sample size using parameters that reflect their clinical application.

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BibTeXRIS

N A Obuchowski. 2000. Sample size tables for receiver operating characteristic studies.. https://doi.org/10.2214/ajr.175.3.1750603

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A receiver operating characteristic partial area index for highly sensitive diagnostic tests.

PURPOSE: Area under a receiver operating characteristic (ROC) curve (Az) is widely used as an index of diagnostic performance. However, Az is not a meaningful summary of clinical diagnostic performance when high sensitivity must be maintained clinically. The authors developed a new ROC partial area index, which measures clinical diagnostic performance more meaningfully in such situations, to summarize an ROC curve in only a high-sensitivity region. MATERIALS AND METHODS: The mathematical formation of the partial area index was derived from the conventional binormal model. Statistical tests of apparent differences in this index were formulated analogous to that of Az. One common statistical test involving the partial area index was validated by computer simulations under realistic conditions. RESULTS: An example in mammography illustrates a situation in which the partial area index is more meaningful than Az in measuring clinical diagnostic performance. CONCLUSION: The partial area index can be used as a more meaningful alternative to the conventional Az index for highly sensitive diagnostic tests.

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