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

Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results from...

You might also read

Related Articles

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

Sort by
Same author

The use of religion, prayer, and faith to cope with the receipt of Alzheimer's disease risk information in a research context: A qualitative study of Latinos in NYC.

SSM. Qualitative research in health·2026
Same author

Latinos' beliefs regarding the role played by nonmedical factors in the quality of Alzheimer's disease care: Findings from a NYC community-based sample.

SSM. Qualitative research in health·2026
Same author

Memory and thinking problems that aging Latinos in New York City would bring to a doctor's attention.

PloS one·2026
Same author

Latinos' appraisals of and responses to memory problems.

Journal of health psychology·2026
Same author

Exploring immediate responses to <i>APOE</i><b>ε</b>4/ε4 genotype and Alzheimer's disease risk disclosure in Latinos.

Journal of Alzheimer's disease : JAD·2026
Same author

Family history, perceived risk, and perceived threat of Alzheimer's disease among Latino residents of northern Manhattan.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026

Related Experiment Video

Updated: May 29, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Causal models for investigating complex disease: I. A primer.

Ann M Madsen1, Susan E Hodge, Ruth Ottman

  • 1Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA.

Human Heredity
|September 14, 2011
PubMed
Summary

Causal models offer a powerful framework for genetic epidemiology research, clarifying cause-and-effect relationships at the individual level. This approach enhances the identification of causal genes and improves understanding of genetic and environmental interactions.

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

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

Published on: June 21, 2018

Related Experiment Videos

Last Updated: May 29, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

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

Published on: June 21, 2018

Area of Science:

  • Genetic Epidemiology
  • Statistical Genetics
  • Causal Inference

Background:

  • Causal models are underutilized in genetic epidemiology despite their application in other fields.
  • Traditional statistical models in genetic epidemiology lack explicit cause-and-effect formulations at the individual level.

Purpose of the Study:

  • To demonstrate the utility of causal models in genetic epidemiology and statistical genetics.
  • To frame key genetic epidemiology concepts within causal modeling frameworks.
  • To explore the application of causal models for understanding confounding and bias in genetic studies.

Main Methods:

  • Description of the sufficient component cause model and potential outcomes model.
  • Application of causal models to concepts like penetrance, phenocopies, and gene-environment interactions.
  • Illustration of how potential outcomes models address confounding and bias.

Main Results:

  • Causal models elucidate the link between underlying causal mechanisms and observed statistical measures.
  • The study demonstrates how causal models can be applied to various genetic epidemiology concepts.

Conclusions:

  • Causal models significantly enhance research for identifying causal genes.
  • Adoption of causal models can advance the field of genetic epidemiology.