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 Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...

You might also read

Related Articles

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

Sort by
Same author

Tumor miRNA Signatures Associate with Outcomes of Patients with Stage II/III Melanoma.

Clinical cancer research : an official journal of the American Association for Cancer Research·2025
Same author

Two-stage subsampling variable selection for sparse high-dimensional generalized linear models.

Statistical methods in medical research·2025
Same author

A New Strategy for Evaluating the Impact of Epidemiologic Risk Factors for Cancer With Application to Melanoma.

Journal of the American Statistical Association·2025
Same author

Association of Inherited Genetic Variants with Multiple Primary Melanoma.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2025
Same author

Sex differences in melanoma survival-a GEM study.

JNCI cancer spectrum·2025
Same author

Improving Accuracy of Somatic Mutation Profiling in Large Epidemiologic Studies: Addressing Cases without Matched Normal Samples.

bioRxiv : the preprint server for biology·2024

Related Experiment Video

Updated: May 9, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Testing the incremental predictive accuracy of new markers.

Colin B Begg1, Mithat Gonen, Venkatraman E Seshan

  • 1Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, USA.

Clinical Trials (London, England)
|July 25, 2013
PubMed
Summary

Using receiver operating curve (ROC) methods to assess new risk markers with nested models is invalid. Simulations show this practice leads to biased inferences due to correlated data and misinterpretation of marker effects.

More Related Videos

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Related Experiment Videos

Last Updated: May 9, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Informatics

Background:

  • Receiver operating curve (ROC) methodology is commonly used to evaluate new predictive markers.
  • Concerns exist regarding the validity of using ROC curves with nested models.

Purpose of the Study:

  • To critically evaluate the validity of using ROC methodology for assessing incremental predictive accuracy.
  • To identify potential biases when using risk predictors from nested models.

Main Methods:

  • Conducting detailed simulations to assess the performance of ROC analysis.
  • Examining statistical properties of nested models and marker data.

Main Results:

  • Simulations demonstrate that using risk predictors from nested models in ROC tests yields invalid inferences.
  • Key issues identified include strong case-to-case data correlation and biased interpretation of marker effects.

Conclusions:

  • Strongly advise against employing ROC methods with risk predictors from nested regression models.
  • This approach may lead to profound bias when testing the incremental information of new markers.