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

Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

443
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
443
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

414
An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
414
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

573
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...
573
Graphs of Functions01:30

Graphs of Functions

546
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
546
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

1.1K
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...
1.1K
pV-Diagrams01:18

pV-Diagrams

4.6K
The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
4.6K

You might also read

Related Articles

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

Sort by
Same author

How do PhD candidates perceive good research practices in the Netherlands Code of Conduct for Research Integrity?

Research integrity and peer review·2026
Same author

Identifying predictors of early trial termination: a meta-epidemiological study utilising elements of the research ethics committee evaluation.

Journal of clinical epidemiology·2025
Same author

Off-label thrombolysis in acute ischemic stroke patients: Frequencies and outcome compared to on-label and no treatment.

European stroke journal·2025
Same author

Difficulties in Care and Unmet Needs from the Perspective of Patients with Lung Cancer and Stroke - A Qualitative Study in Germany.

Patient preference and adherence·2025
Same author

Coagulation Factors and White Matter Hyperintensities in Middle-Aged Women With and Without Migraine and Ischemic Stroke.

European journal of neurology·2025
Same author

Neuropsychiatric symptoms with focus on apathy and irritability in sporadic and hereditary cerebral amyloid angiopathy.

Alzheimer's research & therapy·2024

Related Experiment Video

Updated: Apr 22, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

4.7K

Graphical presentation of confounding in directed acyclic graphs.

Marit M Suttorp1, Bob Siegerink1, Kitty J Jager2

  • 1Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

Nephrology, Dialysis, Transplantation : Official Publication of the European Dialysis and Transplant Association - European Renal Association
|October 18, 2014
PubMed
Summary

Directed acyclic graphs (DAGs) help identify confounding in observational studies. These visual tools improve causal inference in nephrology research, offering advantages over traditional methods for complex questions.

Keywords:
DAGscausalconfoundingdirected acyclic graphepidemiology

More Related Videos

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.5K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

1.7K

Related Experiment Videos

Last Updated: Apr 22, 2026

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

4.7K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

7.5K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

1.7K

Area of Science:

  • Epidemiology
  • Nephrology
  • Causal Inference

Background:

  • Confounding hinders valid causal inferences from observational data.
  • Directed acyclic graphs (DAGs) are increasingly utilized in epidemiology.
  • DAGs visually represent causal assumptions.

Purpose of the Study:

  • To explain the basic concepts of DAGs.
  • To demonstrate the application of DAGs in nephrology research.
  • To compare DAGs with traditional methods for identifying confounding.

Main Methods:

  • Explanation of Directed Acyclic Graph (DAG) concepts.
  • Application of DAGs to nephrology research examples.
  • Comparison of DAGs with traditional confounding identification methods.

Main Results:

  • DAGs visually represent causal relationships and assumptions.
  • Examples illustrate the identification of confounding in nephrology using DAGs.
  • DAGs can be more effective than traditional methods for complex research.

Conclusions:

  • DAGs are valuable tools for identifying confounding in observational studies.
  • The structured approach of DAGs enhances scientific discussion.
  • DAGs offer a preferable method for addressing confounding, particularly in complex research scenarios.