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

2.1K
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...
2.1K
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

1.6K
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:
1.6K
Censoring Survival Data01:09

Censoring Survival Data

654
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
654
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

489
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.
489
Longitudinal Research02:20

Longitudinal Research

13.7K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
13.7K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

548
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...
548

You might also read

Related Articles

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

Sort by
Same author

Counternull sets in randomized experiments.

The American statistician·2025
Same author

The Association between Multiple Environmental Exposures and Symptoms of Generalized Anxiety Disorder in a Prospective, US-Based Cohort Study.

Environmental health perspectives·2025
Same author

Performance of algorithms using wrist temperature for retrospective ovulation day estimate and next menses start day prediction: a prospective cohort study.

Human reproduction (Oxford, England)·2025
Same author

United we thrive: friendship and subsequent physical, behavioural and psychosocial health in older adults (an outcome-wide longitudinal approach).

Epidemiology and psychiatric sciences·2023
Same author

The road to postpandemic recovery in the USA: a repeated cross-sectional survey of multidimensional well-being over two years.

Public health·2023
Same author

Prenatal and postnatal exposures to endocrine disrupting chemicals and timing of pubertal onset in girls and boys: a systematic review and meta-analysis.

Human reproduction update·2022

Related Experiment Video

Updated: Apr 5, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.5K

Causal mediation analysis for longitudinal data with exogenous exposure.

M-A C Bind1, T J Vanderweele2, B A Coull2

  • 1Departments of Biostatistics, Epidemiology, and Environmental Health, Harvard School of Public Health, Boston, MA 02115, USA ma.bind@mail.harvard.edu.

Biostatistics (Oxford, England)
|August 15, 2015
PubMed
Summary

This study introduces a causal mediation analysis framework for longitudinal data, enabling the examination of exposure-mediator interactions. Findings show direct effects of air pollution and temperature on ICAM-1, with temperature also having an indirect effect via DNA methylation.

Keywords:
Causal mediation analysisGeneralized mixed-effects modelLongitudinal data

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.9K

Related Experiment Videos

Last Updated: Apr 5, 2026

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.5K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.9K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Environmental Health

Background:

  • Mediation analysis is crucial for understanding causal pathways in epidemiological research.
  • Longitudinal data from prospective cohort studies are vital for investigating biological mechanisms.
  • Existing mediation formulae for longitudinal data have limitations regarding exposure-mediator interactions.

Purpose of the Study:

  • To formalize natural direct and indirect effects in a causal framework with potential outcomes, allowing for exposure-mediator interactions.
  • To develop a robust mediation analysis method for longitudinal data using generalized mixed-effects models.
  • To extend the approach for multiple mediators and derive jointly mediated effects.

Main Methods:

  • Utilized a causal framework with potential outcomes to define direct and indirect effects.
  • Employed two generalized mixed-effects models for longitudinal mediator and outcome data.
  • Incorporated random intercepts and slopes for exposure, mediator, and their interaction in the outcome model.

Main Results:

  • The methodology was applied to the Normative Aging Study data.
  • Estimated direct and indirect effects of air pollution and temperature on intercellular adhesion molecule 1 (ICAM-1) protein levels via DNA methylation.
  • Air pollution and temperature showed a direct effect on ICAM-1, while temperature also demonstrated an indirect effect through DNA methylation.

Conclusions:

  • The developed causal mediation framework effectively analyzes longitudinal data with exposure-mediator interactions.
  • The findings highlight distinct pathways through which environmental factors influence biological markers.
  • The approach provides a valuable tool for dissecting complex causal relationships in environmental epidemiology.