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

A marginal regression modelling framework for evaluating medical diagnostic tests

W Leisenring1, M S Pepe, G Longton

  • 1Division of Public Health Sciences and Clinical Statistics, Fred Hutchinson Cancer Research Center, Seattle, Washington 98104, USA.

Statistics in Medicine
|June 15, 1997
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

Independent contribution of bronchoalveolar lavage and serum galactomannan in the diagnosis of invasive pulmonary aspergillosis.

Transplant infectious disease : an official journal of the Transplantation Society·2014
Same author

Male infertility in long-term survivors of pediatric cancer: a report from the childhood cancer survivor study.

Journal of cancer survivorship : research and practice·2014
Same author

Summaries for patients. Increased risk for gastrointestinal cancer in childhood cancer survivors.

Annals of internal medicine·2012
Same author

Semiparametric methods for evaluating the covariate-specific predictiveness of continuous markers in matched case-control studies.

Journal of the Royal Statistical Society. Series C, Applied statistics·2011
Same author

Treatment of Fanconi anemia patients using fludarabine and low-dose TBI, followed by unrelated donor hematopoietic cell transplantation.

Bone marrow transplantation·2010
Same author

A parametric ROC model-based approach for evaluating the predictiveness of continuous markers in case-control studies.

Biometrics·2009
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 marginal regression models for evaluating diagnostic tests, improving sensitivity and specificity analysis. This flexible method enhances disease detection strategies by accounting for covariates and correlated data.

Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Epidemiology

Background:

  • Current statistical methods for evaluating diagnostic tests are limited in their flexibility and scope.
  • Technological advancements necessitate improved statistical approaches for early disease detection.
  • Understanding the impact of covariates on diagnostic test performance is crucial for optimizing screening programs.

Purpose of the Study:

  • To propose and illustrate the use of marginal regression models with robust sandwich variance estimators for diagnostic test evaluation.
  • To demonstrate the flexibility of this method in comparing multiple tests, handling correlated data, and assessing covariate effects.
  • To provide a generalized statistical framework that encompasses standard methods like McNemar's test.

Main Methods:

Related Experiment Videos

  • Application of marginal regression models with robust sandwich variance estimators.
  • Inference on sensitivity and specificity of diagnostic tests.
  • Analysis of correlated data and evaluation of covariate effects on test performance.
  • Comparison with existing methods, including McNemar's test for paired data.

Main Results:

  • Marginal regression models offer a more flexible approach to diagnostic test evaluation compared to standard methods.
  • The proposed methodology allows for the assessment of how patient and environmental characteristics influence test sensitivity and specificity.
  • The method was illustrated using data from an audiology screening study and a cytomegalovirus PCR diagnostic study.

Conclusions:

  • Marginal regression models provide a powerful and adaptable tool for statistical inference in diagnostic test evaluation.
  • This approach can lead to optimized diagnostic testing programs by identifying and leveraging the effects of controllable factors.
  • The methodology generalizes existing statistical techniques, offering a unified framework for analyzing diagnostic accuracy.