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

Randomized Experiments01:13

Randomized Experiments

7.1K
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...
7.1K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

144
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...
144
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

232
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...
232
What is an Experiment?01:12

What is an Experiment?

12.0K
An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
12.0K
Group Design02:01

Group Design

9.2K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
9.2K
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

361
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
361

You might also read

Related Articles

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

Sort by
Same author

Pediatric healthcare worker perspectives on implementation of a secure firearm storage program: a qualitative study.

BMC pediatrics·2026
Same author

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Afternoon panel discussion).

Clinical trials (London, England)·2026
Same author

Nudging implementation of low tidal volume ventilation: a stepped wedge, cluster randomized trial.

Implementation science : IS·2026
Same author

A mixed methods evaluation of mechanisms for facilitation in pediatric primary care.

Implementation science communications·2026
Same author

STRONGER INSTRUMENTS VIA INTEGER PROGRAMMING IN AN OBSERVATIONAL STUDY OF LATE PRETERM BIRTH OUTCOMES.

The annals of applied statistics·2026
Same author

On the mixed-model analysis of covariance in cluster-randomized trials.

Statistical science : a review journal of the Institute of Mathematical Statistics·2026

Related Experiment Video

Updated: Aug 23, 2025

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.3K

Discovering Heterogeneous Exposure Effects Using Randomization Inference in Air Pollution Studies.

Kwonsang Lee1, Dylan S Small2, Francesca Dominici3

  • 1Department of Statistics, Sungkyunkwan University, Seoul, Republic of Korea.

Journal of the American Statistical Association
|October 31, 2022
PubMed
Summary
This summary is machine-generated.

Long-term exposure to fine particulate matter (PM2.5) increases mortality risk, especially for low-income seniors aged 81-85 and those 85+. Targeted interventions are crucial for protecting vulnerable populations from air pollution effects.

Keywords:
Causal effectCausal inferenceObservational studyParticulate MatterRecursive partitioningSample splitUnmeasured confounding

More Related Videos

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity
08:16

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Published on: March 13, 2014

19.0K
Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.2K

Related Experiment Videos

Last Updated: Aug 23, 2025

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.3K
Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity
08:16

Experimental Protocol for Manipulating Plant-induced Soil Heterogeneity

Published on: March 13, 2014

19.0K
Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.2K

Area of Science:

  • Environmental Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Long-term exposure to air pollution, even at low levels, is linked to increased mortality risk.
  • Existing regulatory actions are costly, necessitating targeted interventions for vulnerable groups.
  • Identifying subgroups with differential exposure effects is critical for effective public health strategies.

Purpose of the Study:

  • To introduce a novel statistical method for discovering subgroups with heterogeneous exposure effects.
  • To develop methods for assessing the robustness of findings to unmeasured confounding.
  • To increase statistical power for detecting exposure effect heterogeneity.

Main Methods:

  • Developed a novel statistical method, 'denovo', to identify subgroups with differing exposure effects.
  • Employed randomization-based tests to assess discovered heterogeneous effects.
  • Conducted sensitivity analyses to evaluate robustness against unmeasured confounding bias.

Main Results:

  • The 'denovo' method demonstrated increased statistical power to detect heterogeneity in exposure effects via simulations.
  • Applied the method to Medicare data (1,612,414 beneficiaries, 2000-2006) in New England.
  • Found significantly greater causal effects of PM2.5 on 5-year mortality for seniors aged 81-85 with low income and those 85+.

Conclusions:

  • The novel statistical approach effectively identifies vulnerable subgroups with heightened mortality risks from PM2.5 exposure.
  • Findings highlight specific elderly demographics (low-income, 81-85 and 85+) disproportionately affected by air pollution.
  • Results support the development of targeted public health interventions to mitigate air pollution's impact on susceptible populations.