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

Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

1.4K
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:
1.4K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

674
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...
674
Causality in Epidemiology01:21

Causality in Epidemiology

1.8K
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...
1.8K
Correlation and Causation01:27

Correlation and Causation

43.5K
Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
43.5K
Cross-Sectional Research01:50

Cross-Sectional Research

12.8K
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
12.8K
Correlation of Experimental Data01:23

Correlation of Experimental Data

500
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
500

You might also read

Related Articles

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

Sort by
Same author

Structural Nested Mean Models for Modified Treatment Policies.

Statistics in medicine·2026
Same author

Long COVID longitudinal symptoms burden clusters within a national community-based cohort.

BMC infectious diseases·2026
Same author

Effect of a third COVID-19 vaccine dose on the incidence of Long COVID among adults who completed a primary vaccine series: a target trial emulation in a community-based cohort.

Vaccine·2026
Same author

Discussion on ''Nonparanormal Adjusted Marginal Inference'' by Susanne Dandl and Torsten Hothorn.

Biometrics·2026
Same author

Natural history of self-reported symptoms following SARS-CoV-2 infection: A target trial emulation in a prospective community-recruited cohort.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2026
Same author

Natural History of Self-reported Symptoms Following SARS-CoV-2 Infection: A Target Trial Emulation in a Prospective Community-Based Cohort.

medRxiv : the preprint server for health sciences·2025
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Mar 1, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Using Causal Diagrams to Assess Parallel Trends in Difference-in-Differences Studies.

Audrey Renson1, Oliver Dukes2, Zach Shahn3

  • 1Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA.

Statistics in Medicine
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study provides guidance on assessing the parallel trends assumption in difference-in-differences (DID) analysis. We link causal diagrams to parallel trends, offering conditions to reject or accept this key assumption for robust causal inference.

Keywords:
causal inferenceconfoundingdifference‐in‐differencesdirected acyclic graphssingle world intervention graphs

More Related Videos

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.4K
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

6.8K

Related Experiment Videos

Last Updated: Mar 1, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.4K
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

6.8K

Area of Science:

  • Causal Inference
  • Econometrics
  • Health Services Research

Background:

  • Difference-in-differences (DID) is a widely used quasi-experimental method.
  • The validity of DID relies heavily on the parallel trends assumption.
  • Guidance for a priori assessment of the parallel trends assumption is limited.

Purpose of the Study:

  • To develop criteria for evaluating the plausibility of the parallel trends assumption.
  • To connect nonparametric causal diagrams with the scale-dependent parallel trends assumption.
  • To provide practical guidance for difference-in-differences studies.

Main Methods:

  • Utilized causal diagrams to derive conditions for parallel trends.
  • Introduced a linear faithfulness assumption.
  • Analyzed the relationship between pre-treatment and post-treatment outcomes and unmeasured confounders.

Main Results:

  • Established conditions to reject parallel trends: treatment affected by pre-treatment outcomes, or specific unmeasured confounder structures.
  • Identified situations where parallel trends should be questioned: pre-treatment outcomes affecting post-treatment outcomes.
  • Defined a necessary and sufficient condition for parallel trends when other violations are absent.

Conclusions:

  • The study offers a framework for assessing the parallel trends assumption using causal diagrams.
  • This guidance can improve the reliability of difference-in-differences estimates.
  • The approach is illustrated using the example of Medicaid expansion's effect on health insurance coverage.