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

190
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...
190
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

343
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...
343
Multiple Regression01:25

Multiple Regression

3.4K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.4K
Regression Toward the Mean01:52

Regression Toward the Mean

6.6K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.6K
Regression Analysis01:11

Regression Analysis

6.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
6.7K
Randomized Experiments01:13

Randomized Experiments

8.5K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.5K

You might also read

Related Articles

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

Sort by
Same author

Nothing to See Here? A Non-Inferiority Approach to Parallel Trends.

Statistics in medicine·2026
Same author

Regionalization of Hip Fracture Care in Five High-Income Countries.

Health services research·2025
Same author

Time on Your Side: Aggregating Data in Difference-In-Differences Studies.

Health services research·2025
Same author

State Bans on Sexual Orientation and Gender Identity Change Efforts and Youth Suicidality.

Health services research·2025
Same author

Transporting difference-in-differences estimates to assess health equity impacts of payment and delivery models.

Health services research·2024
Same author

Sex-Based Disparities in Acute Myocardial Infarction Treatment Patterns and Outcomes in Older Adults Hospitalized Across 6 High-Income Countries: An Analysis From the International Health Systems Research Collaborative.

Circulation. Cardiovascular quality and outcomes·2024
Same journal

The Association Between Sepsis Coding and Payment to U.S. Hospitals.

Health services research·2026
Same journal

Stagnation in Achieving Recommended Methadone Doses in Opioid Use Disorder Treatment.

Health services research·2026
Same journal

Promoting Transplant Access Through Dialysis Facility Performance Metrics: A Double-Edged Sword.

Health services research·2026
Same journal

Understanding Medicaid Estate Recovery: The Experience of North Carolina and Policy Implications for Future Reforms.

Health services research·2026
Same journal

Racial Disparities and Personal Responsibility Incentives in Medicaid.

Health services research·2026
Same journal

Under-Documentation of Z-Codes in Hospitalizations of Homeless Shelter Users in New York City.

Health services research·2026
See all related articles

Related Experiment Video

Updated: Nov 6, 2025

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.1K

Confounding and regression adjustment in difference-in-differences studies.

Bret Zeldow1, Laura A Hatfield2

  • 1Department of Mathematics and Statistics, Colby College, Waterville, Maine, USA.

Health Services Research
|May 12, 2021
PubMed
Summary
This summary is machine-generated.

Confounding bias in difference-in-difference studies arises from covariates that change over time or affect outcomes differently. Properly accounting for these confounders using causal models and appropriate analytical techniques is crucial for unbiased treatment effect estimation.

Keywords:
difference-in-differencesmatchingparallel trendsregression adjustmenttime-varying confounding

More Related Videos

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

15.7K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K

Related Experiment Videos

Last Updated: Nov 6, 2025

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.1K
Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

15.7K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Economics

Background:

  • Difference-in-differences (DiD) is a quasi-experimental method used to estimate treatment effects.
  • Confounding bias can arise in DiD studies, particularly with time-varying confounders.
  • Existing methods for addressing confounding in cross-sectional studies may not directly translate to DiD settings.

Purpose of the Study:

  • To define confounding bias in difference-in-difference studies.
  • To compare regression- and matching-based estimators for correcting bias from observed confounders.
  • To provide guidance on modeling time-varying confounders in DiD analysis.

Main Methods:

  • Simulated data from linear models with various confounding relationships (time-invariant/varying covariates, constant/varying effects).
  • Evaluated six model specifications, including linear regression and matching techniques.
  • Assessed bias and root mean squared error of treatment effect estimates.

Main Results:

  • Confounders in DiD studies include covariates with differential time trends or time-varying outcome effects.
  • Adjusting for measured confounders in a correctly specified causal model can yield unbiased estimates.
  • Estimating unbiased causal effects is challenging when time-varying confounders are affected by treatment.

Conclusions:

  • Confounding in DiD is complex and requires careful consideration of causal relationships.
  • Postulating a causal model is essential to guide the selection of appropriate analytical methods (regression, matching).
  • Thoughtful covariate incorporation is key to mitigating confounding bias in DiD studies.