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

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

484
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:  
484
Bias01:22

Bias

4.7K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
4.7K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

135
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...
135
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

156
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
156
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.8K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.8K
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

464
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
464

You might also read

Related Articles

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

Sort by
Same author

Analysis of factors associated with persistent night pain after locking plate fixation of proximal humerus fractures and development of a prediction model.

BMC musculoskeletal disorders·2026
Same author

VirBinn improves viral genome binning from metagenomic Hi-C through graph diffusion.

Bioinformatics (Oxford, England)·2026
Same author

Arabidopsis PP2C clade B members are negative feedback regulators of MPK3/MPK6 MAPK cascade in plant immunity and development.

Plant physiology·2026
Same author

The Kelch-Repeat Superfamily Gene <i>SiNL4</i> Regulates the Leaf Width in Foxtail Millet.

Plants (Basel, Switzerland)·2026
Same author

Post-Marketing Safety of mRNA Vaccines: A Real-World Study Integrating Literature Case Reports and Vaccine Adverse Event Reporting System.

Vaccines·2026
Same author

Substrate affinity and spatial proximity synergistically guide multi-enzyme architecture rewiring for highly efficient chitin conversion.

Bioresource technology·2026

Related Experiment Video

Updated: Aug 14, 2025

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

Quantitative bias analysis of prevalence under misclassification: evaluation indicators, calculation method and case

Jin Liu1, Shiyuan Wang2, Fang Shao3

  • 1Clinical Research Institute, First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

International Journal of Epidemiology
|January 10, 2023
PubMed
Summary

Misclassification bias in prevalence estimates is often overlooked. New quantitative bias analysis (QBA) indicators help researchers assess and manage this bias, improving epidemiological study reliability.

Keywords:
Quantitative bias analysisevaluation indicatorsmisclassification biasprevalence

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

7.6K
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

Related Experiment Videos

Last Updated: Aug 14, 2025

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
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

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Prevalence estimates are crucial in epidemiology but susceptible to misclassification bias.
  • Assessing this bias risk is often neglected due to a lack of knowledge and tools.

Purpose of the Study:

  • To introduce novel quantitative bias analysis (QBA) indicators for assessing misclassification bias in prevalence estimates.
  • To provide practical tools and real-world examples for applying these indicators.

Main Methods:

  • Proposed three new indicators based on QBA principles: relative bias, critical point of zero bias, and bound of positive test proportion.
  • Illustrated indicator characteristics and calculation methods using three real-world epidemiological cases.
  • Developed an Excel-based tool to facilitate the application of these methods.

Main Results:

  • Demonstrated that minor variations in positive test proportion can significantly amplify misclassification bias.
  • Highlighted the critical need for analytical consideration of misclassification error in interpreting adjusted prevalence.
  • Showcased the utility of the proposed indicators in quantifying bias magnitude, direction, and uncertainty.

Conclusions:

  • Researchers must analytically account for misclassification error when interpreting prevalence estimates for epidemiological decision-making.
  • The proposed QBA indicators and accompanying tool enhance the assessment and management of misclassification bias.
  • Increased application of QBA methods is vital for improving the accuracy and reliability of epidemiological research.