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

Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

334
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...
334
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.2K
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:  
1.2K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

376
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,...
376
Bias01:22

Bias

7.2K
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...
7.2K
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

552
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
552
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

225
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
225

You might also read

Related Articles

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

Sort by
Same author

Pregnancy Weight Gain and Longer-Term Maternal Cardiometabolic Conditions.

Hypertension (Dallas, Tex. : 1979)·2026
Same author

Development and Validation of a Prediction Model for Cardiovascular Risk in Reproductive-Aged Women.

JACC. Advances·2026
Same author

Incident Hypertension in Young Adults With a Mild Estimated Glomerular Filtration Rate Reduction.

Journal of the American Heart Association·2026
Same author

The potential of a national school food program to reduce dietary inequalities among children in Canada.

Canadian journal of public health = Revue canadienne de sante publique·2026
Same author

Evaluating the Joint Effects of Dementia and Frailty on Burdensome Transitions Among Long-Term Care Residents in Ontario, Canada.

Journal of the American Geriatrics Society·2026
Same author

Data resource profile: a nationally representative linked pregnancy cohort in Canada integrating clinical, social, and environmental data.

International journal of population data science·2026
Same journal

Cost-effectiveness of Pharmacist- and Nurse Practitioner-led Medication Management for Heart Failure With Reduced Ejection Fraction.

The Canadian journal of cardiology·2026
Same journal

Rethinking Pacing After Transcatheter Aortic Valve Implantation to Preserve Ventricular Function.

The Canadian journal of cardiology·2026
Same journal

High Intensity Interval Training Versus Moderate Continuous Training in Adults with Congenital Heart Disease: A Randomized Controlled Trial.

The Canadian journal of cardiology·2026
Same journal

Four-Chamber Myocardial Strain to Predict Mortality in Pulmonary Embolism.

The Canadian journal of cardiology·2026
Same journal

Transcatheter Exclusion of a Dual-Channel Aortic Root Pseudoaneurysm with Paravalvular Regurgitation Using Two Vascular Plugs: A Case Report.

The Canadian journal of cardiology·2026
Same journal

Cardiac Computed Tomography-Guided Procedural Planning for Percutaneous Mitral Paravalvular Leak Closure: Impact on Crossing Time.

The Canadian journal of cardiology·2026
See all related articles

Related Experiment Video

Updated: Jan 7, 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

15.0K

Probabilistic Quantitative Bias Analysis for Misclassification and Uncontrolled Confounding: A Methodological

Nicholas Grubic1, Amy Johnston2, Sonia M Grandi3

  • 1Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.

The Canadian Journal of Cardiology
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

Quantitative bias analysis (QBA) adjusts for errors in observational studies. This method improved the reliability of the obesity-hypertension association by correcting for misclassification and confounding.

Keywords:
biasconfoundingepidemiologyhypertensionmisclassificationobesity

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

2.5K
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

7.9K

Related Experiment Videos

Last Updated: Jan 7, 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

15.0K
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

2.5K
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

7.9K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Cardiovascular Research

Background:

  • Observational studies and clinical trials are susceptible to various biases.
  • Bias can distort findings, impacting the reliability of research conclusions.
  • Quantitative bias analysis (QBA) provides a method to adjust for these distortions.

Purpose of the Study:

  • To demonstrate the application of probabilistic QBA.
  • To adjust for exposure misclassification, outcome misclassification, and uncontrolled confounding.
  • To examine the association between obesity and hypertension as an exemplar.

Main Methods:

  • Utilized National Health and Nutrition Examination Survey data, split into analysis and validation sets.
  • Introduced misclassification using self-reported vs. objective measures for obesity and hypertension.
  • Incorporated uncontrolled confounding by omitting socioeconomic status adjustment.
  • Applied Monte Carlo-based probabilistic QBA to derive bias-adjusted measures.

Main Results:

  • Correcting for obesity misclassification increased association estimates, indicating attenuation by self-reported data.
  • Adjusting for hypertension misclassification generally decreased estimates, except in middle-aged males.
  • Accounting for socioeconomic confounding increased estimates, especially in older adults, revealing residual confounding.

Conclusions:

  • QBA is a practical method for assessing and adjusting for bias in research.
  • This approach enhances the interpretation and reliability of findings, particularly in cardiovascular studies.
  • QBA improves the robustness of epidemiological evidence.