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

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...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
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...
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Group Design02:01

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...

You might also read

Related Articles

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

Sort by
Same author

MIND diet moderates the associations between cerebrovascular and neurodegenerative disease burden and cognition.

Frontiers in nutrition·2026
Same author

Pentagalloylglucose and tannic acid improve intestinal health by protecting the mucus barrier from Clostridium perfringens-induced degradation.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Transcobalamin II deficiency mimicking myelodysplastic syndrome in a child: a case report.

Frontiers in pediatrics·2026
Same author

Impact of Antifibrotic Adherence and Dosing on Risk of Mortality and Hospitalization in Idiopathic Pulmonary Fibrosis: A Nested Case-Control Study.

Chest·2026
Same author

Development of a Taste-Masked, Dose-Flexible, Multiparticulate Pediatric Dosage Form: Case Study of Crizotinib, a Challenging Pediatric Formulation.

Pharmaceutical medicine·2026
Same author

Interplay between Donor Identity and Sulfur Oxidation in Regulating the Photophysics of Phenothiazine-Based D-A Molecules.

The journal of physical chemistry. A·2026
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

Related Experiment Video

Updated: May 7, 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

Optimal two-phase sampling design for comparing accuracies of two binary classification rules.

Huiping Xu1, Siu L Hui, Shaun Grannis

  • 1Department of Biostatistics, Indiana University School of Public Health and School of Medicine, Indianapolis, IN, U.S.A.

Statistics in Medicine
|September 17, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an optimal sampling design to improve the comparison of two binary classification rules, reducing variance in accuracy measures like sensitivity and specificity. The design prioritizes discordant results for more efficient performance evaluation.

Keywords:
diagnostic accuracydiagnostic testpositive predicted valuerecord linkagesensitivityspecificitystratified sampling

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Related Experiment Videos

Last Updated: May 7, 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

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Statistical methodology
  • Machine learning evaluation

Background:

  • Comparing binary classification rules (e.g., record linkage, screening tests) is crucial for performance assessment.
  • Existing statistical methods often lack optimized sampling schemes, potentially leading to higher variance in performance estimates.
  • The gold standard is typically required for all units or a subsample in two-phase studies, but these schemes are not optimized for variance reduction.

Purpose of the Study:

  • To develop and evaluate an optimal sampling design for comparing the performance of two binary classification rules.
  • To minimize the variance of estimators for key accuracy measures, including sensitivity, specificity, and positive predictive values.
  • To provide a method for optimizing sampling schemes when comparing classification algorithms or diagnostic tests.

Main Methods:

  • Derived analytic variance formulas for estimates of differences in sensitivity, specificity, and positive predictive values.
  • Developed an optimal sampling design based on these variance formulas.
  • Conducted an empirical investigation comparing the optimal design with simple random sampling and proportional allocation.
  • Applied the optimal sampling strategy to a real-world record linkage case study.

Main Results:

  • The optimal sampling design is similar for estimating differences in sensitivities and specificities.
  • Significant variance reduction was achieved by over-sampling subjects with discordant results and under-sampling those with concordant results.
  • The empirical study demonstrated the efficiency of the optimal sampling design compared to traditional methods.
  • A heuristic rule was proposed for situations with limited prior knowledge of classification rule performance or prevalence.

Conclusions:

  • The proposed optimal sampling design effectively reduces variance in comparing binary classification rules.
  • This approach enhances the accuracy and efficiency of evaluating record linkage algorithms and screening tests.
  • The findings offer practical guidance for designing studies that compare classification performance, particularly when optimizing resource allocation.
  • The optimal sampling strategy is valuable for real-world applications requiring precise performance comparisons.