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

Bayesian semi-parametric ROC analysis.

Alaattin Erkanli1, Minje Sung, E Jane Costello

  • 1Department of Biostatistics and Bioinformatics, Duke University Medical School, Box 3454, Durham, NC 27710, USA. al@psych.duhs.duke.edu

Statistics in Medicine
|January 18, 2006
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

Endometrial thickness in patients with postmenopausal bleeding and endometrial cancer: A retrospective cohort study.

Gynecologic oncology reports·2026
Same author

What is the risk of HCC in hypervascular LI-RADS 3 observations without ancillary features in routine clinical practice?

Abdominal radiology (New York)·2026
Same author

Are two really better than one? A retrospective study comparing monotherapy versus combination therapy for <i>Stenotrophomonas maltophilia</i> infections.

Microbiology spectrum·2026
Same author

Pharmacy Technician Impact on Access to High-Cost Heart Failure Pharmacotherapy.

The Annals of pharmacotherapy·2026
Same author

Risk Factors for Breakthrough Cytomegalovirus Infections While on Prophylaxis in Kidney and Pancreas Transplant Recipients.

Transplant infectious disease : an official journal of the Transplantation Society·2026
Same author

The Impact of a Protocol on Equitable Labor and Delivery Substance Use Screening: A Retrospective Cohort Study.

American journal of perinatology·2025
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

This study introduces a Bayesian method using mixtures of Dirichlet process priors (MDP) for accurate receiver operating characteristic (ROC) curve estimation. This approach improves diagnostic accuracy and disease prevalence estimates, especially with non-normal screening scores.

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Medical Diagnostics

Background:

  • Estimating receiver operating characteristic (ROC) curves is crucial for evaluating diagnostic tests.
  • Non-normal distributions in screening scores can lead to inaccurate diagnostic cut-offs and unreliable disease prevalence estimates.
  • Existing methods may struggle with imperfect diagnostic tests that are not gold standards.

Purpose of the Study:

  • To develop a semi-parametric Bayesian approach for ROC curve estimation using mixtures of Dirichlet process priors (MDP).
  • To address challenges in modeling non-standard diagnostic score distributions.
  • To provide a robust method for ROC analysis when diagnostic tests are imperfect.

Main Methods:

  • A semi-parametric Bayesian framework utilizing mixtures of Dirichlet process priors (MDP).

Related Experiment Videos

  • An efficient Gibbs sampling algorithm for posterior computations, based on a finite-dimensional MDP approximation.
  • Application to both simulated and real-world datasets.

    • Main Results:

    • The MDP approach effectively models non-standard distributions common in diagnostic screening.
    • The proposed Bayesian method yields reliable ROC curve estimates.
    • MDP modeling for ROC estimation demonstrates strong parallels with the frequentist kernel density estimation (KDE) approach.

    Conclusions:

    • Mixtures of Dirichlet process priors offer a robust Bayesian solution for ROC curve estimation.
    • This method enhances the reliability of diagnostic accuracy and disease prevalence assessments.
    • The MDP approach provides a valuable alternative for analyzing data from imperfect diagnostic tests.