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

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
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...
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...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

You might also read

Related Articles

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

Sort by
Same author

Image Quality Assessment of Diffusion-Weighted Imaging (DWI) and Its Impact on Apparent Diffusion Coefficient (ADC) as a Quantitative Imaging Biomarker for Predicting Response to Neoadjuvant Chemotherapy in High-Risk Early Breast Cancer.

Tomography (Ann Arbor, Mich.)·2026
Same author

Factors influencing accelerated progression in behavioral variant frontotemporal dementia.

Journal of neurology·2026
Same author

Continuous At-Home Monitoring of Nighttime Bed Behavior in Frontotemporal Dementia.

Neurology open access·2026
Same author

Assessing the associations between immunosuppressant use and COVID-19 severity in patients with autoimmune hepatitis.

World journal of hepatology·2026
Same author

Test-retest reliability of resting-state functional magnetic resonance imaging during deep brain stimulation for Parkinson's disease.

NeuroImage. Clinical·2026
Same author

COVID-19 related brain MRI changes and anticipated long-term dementia risk: A front-door-criterion-inspired approach.

Annals of epidemiology·2026
Same journal

Improving Overall Risk Ranking via Subgroup-Level Information Borrowing in Survival Risk Stratification.

Statistics and its interface·2026
Same journal

High-dimensional Bayesian mediation analysis with adaptive Laplace priors.

Statistics and its interface·2026
Same journal

Imaging mediation analysis for longitudinal outcomes: a case study of childhood brain tumor survivorship.

Statistics and its interface·2025
Same journal

Variable selection for doubly robust causal inference.

Statistics and its interface·2025
Same journal

Smooth online parameter estimation for time varying VAR models with application to rat local field potential activity data.

Statistics and its interface·2025
Same journal

A Double Regression Method for Graphical Modeling of High-dimensional Nonlinear and Non-Gaussian Data.

Statistics and its interface·2025
See all related articles

Related Experiment Video

Updated: May 16, 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

Bayesian decision analysis for choosing between diagnostic/prognostic prediction procedures.

John Kornak1, Ying Lu

  • 1University of California, San Francisco, Department of Radiology and Biomedical Imaging and Department of Epidemiology and Biostatistics, 185 Berry St, Ste. 350, San Francisco, CA 94107, USA.

Statistics and Its Interface
|December 18, 2012
PubMed
Summary
This summary is machine-generated.

Choosing new diagnostic procedures involves balancing accuracy and cost. This study introduces a Bayesian decision approach to optimize this selection, considering both the procedure and its threshold for better medical decision-making in areas like osteoporosis screening.

More Related Videos

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

Related Experiment Videos

Last Updated: May 16, 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

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease
06:16

Signal Acquisition, Score Interpretation, and Economics of a Non-Invasive Point-of-Care Test for Coronary Artery Disease

Published on: August 9, 2024

Area of Science:

  • Medical Decision Making
  • Health Economics
  • Biostatistics

Background:

  • Healthcare institutions constantly evaluate new diagnostic procedures against existing ones.
  • Decisions on adopting new methods often prioritize accuracy and cost-effectiveness but lack a formal decision-theoretic framework.
  • Diagnostic decisions frequently involve continuous variables and thresholding, adding complexity to method selection.

Purpose of the Study:

  • To propose a formal Bayesian decision approach for selecting optimal diagnostic procedures and their thresholds.
  • To incorporate diagnostic accuracy and cost-effectiveness into a unified decision-making framework.
  • To address uncertainty in optimal threshold values for diagnostic tests.

Main Methods:

  • Developed a Bayesian decision framework to simultaneously optimize diagnostic procedure and threshold level.
  • Utilized expected utility maximization, integrating accuracy and costs.
  • Applied the approach to compare bone mineral density (BMD) measurements for osteoporotic hip fracture prevention.

Main Results:

  • The proposed Bayesian approach provides a structured method for evaluating diagnostic procedures.
  • Simultaneous optimization of procedure and threshold can lead to more informed clinical decisions.
  • Demonstrated utility comparison for BMD measurements in elderly patients at risk of hip fracture.

Conclusions:

  • A Bayesian decision framework offers a robust method for selecting diagnostic procedures and thresholds.
  • This approach enhances decision-making by formally considering accuracy, cost, and threshold uncertainty.
  • The application to BMD measurements highlights its practical value in managing osteoporotic hip fracture risk.