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

3.4K
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...
3.4K
Bootstrapping01:24

Bootstrapping

658
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
658
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

260
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%...
260
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

604
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...
604
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

494
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
494
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K

You might also read

Related Articles

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

Sort by
Same author

Partial verification bias correction using scaled inverse probability resampling for binary diagnostic tests.

PloS one·2025
Same author

Factors influencing the success of targeted weight loss in healthcare providers with overweight and obesity after a six-month weight reduction intervention program.

PloS one·2025
Same author

Developing a Supplementary Diagnostic Tool for Breast Cancer Risk Estimation Using Ensemble Transfer Learning.

Diagnostics (Basel, Switzerland)·2023
Same author

Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records.

Diagnostics (Basel, Switzerland)·2022
Same author

Recent Techniques in Determining the Effects of Climate Change on Depressive Patients: A Systematic Review.

Journal of environmental and public health·2022
Same author

Factors Influencing Mammographic Density in Asian Women: A Retrospective Cohort Study in the Northeast Region of Peninsular Malaysia.

Diagnostics (Basel, Switzerland)·2022
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 20, 2025

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

14.6K

Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic Tests.

Wan Nor Arifin1,2, Umi Kalsom Yusof1

  • 1School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia.

Diagnostics (Basel, Switzerland)
|November 26, 2022
PubMed
Summary
This summary is machine-generated.

Inverse probability bootstrap (IPB) sampling effectively corrects partial verification bias (PVB) in diagnostic accuracy studies, offering accurate sensitivity and specificity estimates. While less precise than existing methods, IPB is recommended for future analyses.

Keywords:
correction methoddiagnostic testinverse probability bootstrap samplingpartial verification biaspropensity score

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

2.2K
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

7.6K

Related Experiment Videos

Last Updated: Aug 20, 2025

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

14.6K
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

2.2K
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

7.6K

Area of Science:

  • Medical diagnostics
  • Biostatistics
  • Health research methodology

Background:

  • Diagnostic accuracy studies are crucial for evaluating new medical tests against gold standards.
  • Sensitivity (Sn) and specificity (Sp) are key performance measures for binary diagnostic tests.
  • Partial verification bias (PVB) can significantly bias Sn and Sp estimates due to selective patient verification.

Purpose of the Study:

  • To investigate the utility of Inverse Probability Bootstrap (IPB) sampling for correcting partial verification bias (PVB).
  • To evaluate IPB's performance in estimating sensitivity and specificity for binary diagnostic tests under the missing-at-random assumption.
  • To compare IPB with existing methods for PVB correction using simulated and clinical data.

Main Methods:

  • Adapted Inverse Probability Bootstrap (IPB) sampling for partial verification bias (PVB) correction.
  • Tested and compared the performance of IPB against established PVB correction methods.
  • Utilized both simulated and real-world clinical datasets for evaluation.

Main Results:

  • IPB demonstrated accuracy in estimating sensitivity (Sn) and specificity (Sp), exhibiting low bias.
  • IPB showed higher standard errors (SE), indicating lower precision compared to existing methods.
  • The missing-at-random assumption was applied in the context of binary diagnostic tests.

Conclusions:

  • IPB is a viable method for correcting partial verification bias (PVB) in diagnostic accuracy studies, providing accurate Sn and Sp.
  • While IPB has limitations in precision (higher SE), its accuracy makes it suitable for analyses expecting full data.
  • Further research is recommended to improve the precision of IPB and reduce its standard error (SE).