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

586
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...
586
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

1.4K
The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This...
1.4K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

9.3K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
9.3K
Response Surface Methodology01:16

Response Surface Methodology

891
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
891
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

9.5K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
9.5K
Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

10.1K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
10.1K

You might also read

Related Articles

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

Sort by
Same author

Perfluorooctanoic acid and cancer incidence: an updated investigation of a cohort in the mid-Ohio Valley.

Environment international·2026
Same author

Circulating pre-diagnostic metabolites and risk of hepatocellular carcinoma and intrahepatic cholangiocarcinoma: a population-based study of 12 cohorts.

Journal of the National Cancer Institute·2026
Same author

Artificially Sweetened and Sugar-Sweetened Beverage Intake and Risk of Liver Cancer.

JAMA network open·2026
Same author

Cessation of Betel Quid Chewing, Smoking, and Alcohol Drinking and Risk of Oral Precancer and Oral Cancer.

JCO global oncology·2026
Same author

Sex differences in cancer incidence persist across race and ethnicity.

Biology of sex differences·2026
Same author

Sociodemographic characteristics of populations living near industrial land disposals of known and suspected carcinogens across the United States.

Journal of exposure science & environmental epidemiology·2026

Related Experiment Video

Updated: Apr 19, 2026

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.3K

Estimation of ROC curve with complex survey data.

Wenliang Yao1, Zhaohai Li, Barry I Graubard

  • 1Department of Statistics, The George Washington University, Washington, 20052, DC, U.S.A.; Clinical Biostatistics, MedImmune, LLC, Gaithersburg, 20878, MD, U.S.A.

Statistics in Medicine
|December 30, 2014
PubMed
Summary

This study introduces a new method to accurately estimate the area under the receiver operating characteristic curve (AUC) for complex survey data. This approach accounts for sample weighting and complex sampling designs, improving diagnostic test performance evaluation.

Keywords:
area under the ROC curve (AUC)balanced repeated replicationjackknife variancereceiver operating characteristic (ROC) curvesurvey sampling

More Related Videos

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

2.8K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

881

Related Experiment Videos

Last Updated: Apr 19, 2026

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.3K
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

2.8K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

881

Area of Science:

  • Biostatistics
  • Epidemiology
  • Survey Methodology

Background:

  • Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) are standard for evaluating diagnostic test performance.
  • Existing AUC estimation methods often fail with complex sample designs common in epidemiological studies.
  • Complex sampling (e.g., stratified, cluster sampling) and weighting introduce biases and inflate variances in standard statistical analyses.

Purpose of the Study:

  • To modify a nonparametric method for estimating AUC that incorporates sampling weights.
  • To develop variance estimation techniques for AUC that account for complex survey designs.
  • To evaluate the performance of the proposed AUC estimation methods using simulation and real-world survey data.

Main Methods:

  • Modified a nonparametric method to include sampling weights for AUC estimation.
  • Employed leaving-one-out jackknife and balanced repeated replication methods for variance estimation.
  • Evaluated finite sample properties through simulations and applied methods to the US Hispanic Health and Nutrition Examination Survey data.

Main Results:

  • The proposed weighted nonparametric method provides reliable AUC estimates for complex survey data.
  • The variance estimation techniques effectively account for intra-cluster correlation and sampling weights.
  • Demonstrated utility in comparing diagnostic performance for overweight/obesity prediction using various anthropometric measures.

Conclusions:

  • The developed methods offer a robust approach to AUC estimation and comparison in complex survey settings.
  • This advancement is crucial for accurate diagnostic test evaluation in large-scale epidemiological research.
  • The findings enable better health assessments using data from complex surveys, like the NHANES.