The accuracy of a diagnostic test with continuous-scale results is of high importance in clinical medicine. It is often summarised by the area under the ROC curve (AUC). In this article, we discuss and compare nine non-parametric confidence intervals of the AUC for a continuous-scale diagnostic test. Simulation studies are conducted to evaluate the relative performance of the confidence intervals for the AUC in terms of coverage probability and average interval length. A real example is used to illustrate the application of the recommended methods.

Protein structural alterations, including misfolding and aggregation, are a hallmark of several diseases, including cancer. However, the possible clinical application of protein conformational analysis using infrared spectroscopy to detect cancer-associated structural changes in proteins has not been established yet. The present study investigates the applicability of Fourier transform infrared spectroscopy in distinguishing the sera of healthy individuals and breast cancer patients. The cancer-associated alterations in the protein structure were analyzed by fitting the amide I (1600–1700 cm⁻¹) band of experimental curves, as well as by comparing the ratio of the absorbance values at the amide II and amide III bands, assigning those as the infrared spectral signatures. The snapshot of the breast cancer-associated alteration in circulating DNA and RNA was also evaluated by extending the spectral fitting protocol to the complex region of carbohydrates and nucleic acids, 1140–1000 cm⁻¹. The sensitivity and specificity of these signatures, representing the ratio of the α-helix and β-pleated sheet in proteins, were both 90%. Likewise, the ratio of amides II and amide III (I₁₅₅₆/I₁₂₉₅) had a sensitivity and specificity of 100% and 80%, respectively. Thus, infrared spectroscopy can serve as a powerful tool to understand the protein structural alterations besides distinguishing breast cancer and healthy serum samples.

In medical diagnostic research, medical tests with continuous values are widely employed to distinguish between diseased and non-diseased subjects. The diagnostic accuracy of a medical test can be assessed by using the receiver operating characteristic (ROC) curve of the test. To summarize the ROC curve and determine an optimal cut-off point for test results, the Youden index is commonly used. In particular, the Youden index is optimized over the entire range of values for sensitivity and specificity, which determine the ROC space. However in clinical practice, one may only be interested in the regions of the ROC curve that correspond to low false-positive rates or/and high sensitivities. In this paper, a new summary index for the ROC curve, called the “partial Youden index”, is defined on regions of the ROC space in which sensitivity/specificity values are of practical interest. The traditional Youden index is a special case of the partial Youden index. Various parametric and non-parametric interval estimation methods are proposed for the partial Youden index. Extensive simulation studies are conducted to evaluate the finite sample performances of the proposed methods. A real example is used to illustrate the application of the new methods.