Partial AUC Estimation and Regression

  title={Partial AUC Estimation and Regression},
  author={Lori E. Dodd and Margaret Sullivan Pepe},
Summary.  Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to discriminate diseased from nondiseased states. The partial area under the receiver operating characteristic (ROC) curve is a measure of diagnostic test accuracy. We present an interpretation of the partial area under the curve (AUC), which gives rise to a nonparametric estimator. This estimator is more robust than existing estimators, which… 

Non‐parametric interval estimation for the partial area under the ROC curve

Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to discriminate diseased conditions from non‐diseased

Transformation models for ROC analysis

Receiver operating characteristic (ROC) analysis is one of the most popular approaches for evaluating and comparing the accuracy of medical diagnostic tests. Although various methodologies have been

Some Novel Statistical Inferences

In medical diagnostic studies, the area under the Receiver Operating Characteristic (ROC) curve (AUC) and Youden index are two summary measures widely used in the evaluation of the diagnostic

Semi-Parametric Inference for the Partial Area Under the ROC Curve

Diagnostic tests are central in the field of modern medicine. One of the main factors for interpreting a diagnostic test is the discriminatory accuracy. For a continuous-scale diagnostic test, the

Statistical Evaluation of Medical Tests

Moves of diagnostic performance for binary tests, such as sensitivity, specificity, and predictive values, are introduced, and extensions to the case of continuous-outcome tests are detailed, with special focus on the receiver operating characteristic (ROC) curve and its estimation.

Bootstrap-based procedures for inference in nonparametric ROC regression analysis

The main aim of the paper is to offer new inferential procedures for testing the effect of covariates over the conditional ROC curve within the ROC-GAM context, and to facilitate the application of these new procedures in practice, an R-package is provided and briefly described.

Model Checking for ROC Regression Analysis

C cumulative residual-based procedures to graphically and numerically assess the goodness of fit for some commonly used ROC regression models are developed and shown how specific components of these models can be examined within this framework.

Empirical likelihood-based inferences for the area under the ROC curve with covariates

In the receiver operating characteristic (ROC) analysis, the area under the ROC curve (AUC) is a popular summary index of discriminatory accuracy of a diagnostic test. Incorporating covariates into



An Interpretation for the ROC Curve and Inference Using GLM Procedures

  • M. Pepe
  • Computer Science
  • 2000
It is shown that inference can be achieved with binary regression techniques applied to indicator variables constructed from pairs of test results, one component of the pair being from a diseasedsubject and the other from a non diseased subject.

Semiparametric Regression for the Area Under the Receiver Operating Characteristic Curve

Application of the regression methods for the nonparametric area under the receiver operating characteristic curve, a well-accepted summary measure of classifier accuracy, to evaluate the covariate effects on a new device for diagnosing hearing impairment reveals that the device performs better in more severely impaired subjects and that certain test parameters are key to test performance.

Incorporating the Time Dimension in Receiver Operating Characteristic Curves: A Case Study of Prostate Cancer

Two biomarkers for prostate cancer were considered and it appears that total PSA performed better than the ratio measure at times closer to diagnosis than for the ratio four and two years prior to diagnosis and at the time of diagnosis.

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

A new ROC partial area index is developed, which measures clinical diagnostic performance more meaningfully in such situations, to summarize an ROC curve in only a high-sensitivity region.

Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.

A nonparametric approach to the analysis of areas under correlated ROC curves is presented, by using the theory on generalized U-statistics to generate an estimated covariance matrix.

Identifying Combinations of Cancer Markers for Further Study as Triggers of Early Intervention

A new class of nonparametric algorithms which extends the ROC paradigm to multiple tests is proposed and various combinations of markers are fit to a training sample and evaluated the performance in a test sample using a target region based on a utility function.

The use of the 'binormal' model for parametric ROC analysis of quantitative diagnostic tests.

  • J. Hanley
  • Mathematics
    Statistics in medicine
  • 1996
The findings justify Metz's use of the binormal model in the 'LABROC' software for ROC analyses of laboratory type data even when the raw data may 'look' decidedly non-Gaussian.

The Statistical Evaluation of Medical Tests for Classification and Prediction

This well-organized book can serve as a refresher of basic topics for more statistically sophisticated readers and as a tutorial for those without the background, as well as on the modeling of correlated data with random (mixed)effects models and generalized estimating equations.

A family of nonparametric statistics for comparing diagnostic markers with paired or unpaired data

SUMMARY In this paper we study a broad class of nonparametric statistics for comparing two diagnostic markers. One can compare the sensitivities of these diagnostic markers over restricted ranges of

Semiparametric estimation of regression quantiles with application to standardizing weight for height and age in US children

The appropriate interpretation of measurements often requires standardization for concomitant factors. For example, standardization of weight for both height and age is important in obesity research