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

Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...
Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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...

You might also read

Related Articles

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

Sort by
Same author

Copy number variation of <i>MYLK4</i> gene and its growth traits of <i>Capra hircus</i> (goat).

Animal biotechnology·2019
Same author

Pax 6 Controls Neural Crest Potential of Limbal Niche Cells to Support Self-Renewal of Limbal Epithelial Stem Cells.

Scientific reports·2019
Same author

Making a Genetic Diagnosis in a Level IV Neonatal Intensive Care Unit Population: Who, When, How, and at What Cost?

The Journal of pediatrics·2019
Same author

Preparation of novel molecularly imprinted magnetic graphene oxide and their application for quercetin determination.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences·2019
Same author

Carbogenic Nanozyme with Ultrahigh Reactive Nitrogen Species Selectivity for Traumatic Brain Injury.

Nano letters·2019
Same author

Mathematical modeling of the heterogeneous distributions of nanomedicines in solid tumors.

European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V·2019
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: May 30, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

A robust method using propensity score stratification for correcting verification bias for binary tests.

Hua He1, Michael P McDermott

  • 1Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA.

Biostatistics (Oxford, England)
|August 23, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to correct verification bias in diagnostic test accuracy, improving accuracy when disease status is not verified for all subjects. The propensity score method offers more robust estimation than existing approaches.

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 30, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

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:

  • Biostatistics
  • Medical diagnostics
  • Epidemiology

Background:

  • Diagnostic test accuracy is crucial, commonly measured by sensitivity and specificity.
  • Verification bias arises when disease status is verified in a subset of subjects, leading to biased estimators.
  • Existing bias correction methods often rely on parametric models, susceptible to misspecification with continuous or numerous covariates.

Purpose of the Study:

  • To propose a novel method for correcting verification bias in diagnostic test accuracy.
  • To develop a bias correction technique robust to model misspecification, especially with complex covariate data.
  • To enhance the reliability of sensitivity and specificity estimates in studies with partial disease status verification.

Main Methods:

  • Introduced a propensity score-based method to correct verification bias.
  • Defined propensity score as the probability of verification given test results and covariates.
  • Classified verified samples into homogeneous propensity score subsamples for bias correction.

Main Results:

  • The proposed propensity score method demonstrated greater robustness to model misspecification compared to existing techniques.
  • Simulation studies confirmed the new estimators' reliability even when underlying models were correctly specified.
  • The method effectively corrects for verification bias in diagnostic accuracy studies with partial verification.

Conclusions:

  • The propensity score approach offers a more robust alternative for correcting verification bias in diagnostic test accuracy studies.
  • This method enhances the accuracy of sensitivity and specificity estimates when dealing with missing disease status data.
  • The findings are particularly relevant for studies with continuous or multiple covariates where traditional methods falter.