Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Partial AUC estimation and regression.

Lori E Dodd1, Margaret S Pepe

  • 1Biometric Research Branch, National Cancer Institute, 6130 Executive Blvd, MSC 7434, Rockville, Maryland 20892, USA. doddl@mail.nih.gov

Biometrics
|November 7, 2003
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

MVA-BN monkeypox vaccine safety in a clinical trial in the Democratic Republic of the Congo.

NPJ vaccines·2026
Same author

Assessing Open-world Foundation Models for Zero-shot Skin Segmentation in Clinical Dermatological Photographs.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Reflections on FDA Draft Guidance on Bayesian Methods in Trials-Protecting Scientific Integrity and Evidentiary Standards.

JAMA·2026
Same author

Response-adaptive randomization with imperfect intermediate endpoints.

Clinical trials (London, England)·2026
Same author

Clinical testing of drug treatment shortening in patients with TB using PET/CT imaging of lung lesions.

Science translational medicine·2026
Same author

Maternal and neonatal outcomes after infection with monkeypox virus clade I during pregnancy in DR Congo: a pooled, prospective cohort study.

Lancet (London, England)·2025
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

This study introduces a robust, nonparametric method for evaluating diagnostic test accuracy using the partial area under the receiver operating characteristic curve (AUC). This approach enhances disease diagnosis by providing a more reliable measure of test performance.

Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Health Informatics

Background:

  • Accurate disease diagnosis is essential in healthcare.
  • Evaluating diagnostic and screening tests is crucial for discriminating between diseased and non-diseased states.
  • The partial area under the receiver operating characteristic curve (ROC AUC) is a key metric for diagnostic test accuracy.

Purpose of the Study:

  • To present a novel interpretation of the partial AUC, leading to a nonparametric estimator.
  • To introduce a regression modeling framework for analyzing covariate effects on partial AUC.
  • To compare the robustness and efficiency of the new estimator against existing parametric methods.

Main Methods:

  • Developed a nonparametric estimator for partial AUC based on a new interpretation.

Related Experiment Videos

  • Proposed a regression modeling framework using binary regression methods for inference.
  • Applied the framework to compare two prostate-specific antigen (PSA) biomarkers and assess biomarker accuracy's dependence on time to diagnosis.

    • Main Results:

    • The proposed nonparametric estimator for partial AUC is more robust than existing parametric estimators.
    • Robustness is achieved with only a moderate loss in efficiency compared to parametric methods.
    • The regression framework effectively refines knowledge about test accuracy and covariate effects.

    Conclusions:

    • The novel nonparametric partial AUC estimator offers a more robust measure of diagnostic test accuracy.
    • The regression modeling framework provides a valuable tool for analyzing factors influencing biomarker accuracy.
    • This approach can enhance the evaluation of diagnostic tests, particularly for conditions like prostate cancer.