Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Odds Ratio

Odds Ratio

The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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,...

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...

Relative Risk

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...

Hazard Ratio

Hazard Ratio

The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...

Assumptions of Survival Analysis

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

You might also read

Related Articles

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

Sort by

Same author

Posterior estimation of longitudinal variance components from nonlongitudinal data using Bayesian Gaussian process model.

Genetics·2025

Same author

Five-leaf Generalizations of the D-statistic Reveal the Directionality of Admixture.

Molecular biology and evolution·2024

Same author

Bayesian inference of admixture graphs on Native American and Arctic populations.

PLoS genetics·2023

Same author

Insights into bear evolution from a Pleistocene polar bear genome.

Proceedings of the National Academy of Sciences of the United States of America·2022

Same author

Genome-wide association study implicates CHRNA2 in cannabis use disorder.

Nature neuroscience·2019

Same author

The comparative genomics and complex population history of <i>Papio</i> baboons.

Science advances·2019

Same journal

Correction to: Home dampness and molds and occurrence of respiratory tract infections in the first 27 years of life: the Espoo Cohort Study.

American journal of epidemiology·2026

Same journal

A SIMPLE AND POWERFUL TEST OF VACCINE WANING.

American journal of epidemiology·2026

Same journal

Association Between maternal body mass index, offspring growth and pubertal timing: results from a longitudinal birth cohort study.

American journal of epidemiology·2026

Same journal

Correction to: Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study.

American journal of epidemiology·2026

Same journal

RE: advancing observational research on arts and health: theory-informed approaches using the RADIANCE framework.

American journal of epidemiology·2026

Same journal

Maternal Cesarean Section and Offspring ASD or ADHD Risk: A Nurses' Health Study II Analysis.

American journal of epidemiology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 26, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Sensitivity Analysis on Odds Ratios.

Kalle Leppälä

American Journal of Epidemiology

|June 14, 2023

Summary

This summary is machine-generated.

This study extends Cornfield inequalities to odds ratios, providing new bounds for assessing confounding effects in epidemiological research. These findings enhance sensitivity analysis for odds ratios, complementing existing risk ratio methods.

Keywords:

Cornfield conditions Cornfield inequalities mediant inequality odds ratio sensitivity analysis

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

Related Experiment Videos

Last Updated: Jul 26, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

Area of Science:

Epidemiology
Biostatistics
Causal Inference

Background:

The classical Cornfield inequalities provide bounds for confounding in risk ratio (RR) analyses.
Existing work by Ding and VanderWeele offers assumption-free sensitivity analysis for RRs.
Analogous inequalities for odds ratios (ORs) were lacking, despite ORs' common use and potential conversion issues.

Purpose of the Study:

To present a version of the classical Cornfield inequalities specifically for the odds ratio.
To develop sharp bivariate bounds for observed associations using both risk ratios and odds ratios.

Main Methods:

Derivation of Cornfield inequalities for the odds ratio.
Application of the mediant inequality in the proof.
Development of bivariate bounds for confounding assessment.

Main Results:

A novel formulation of Cornfield inequalities for the odds ratio is presented.
Sharp bivariate bounds are established for confounding assessment using RRs and ORs.
The findings provide a theoretical basis for sensitivity analysis with odds ratios.

Conclusions:

The study bridges a gap in confounding assessment by extending Cornfield inequalities to odds ratios.
The developed bounds offer improved tools for evaluating the impact of unmeasured confounding.
This work enhances the robustness of causal inference in observational studies using odds ratios.