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

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...
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

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 phenomenon...
Cross-Sectional Research01:50

Cross-Sectional Research

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...
Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Observational Studies01:11

Observational Studies

Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One example of...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

You might also read

Related Articles

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

Sort by
Same author

Reliability of the Telehealth Hammersmith Infant Neurological Examination at 24 Months: A Prospective Counterbalanced Observational Study.

Pediatric neurology·2026
Same author

Peritoneal drainage in spontaneous intestinal perforation: a 20-year single center review.

Pediatric surgery international·2026
Same author

From Slices to Surfaces: The Feasibility of Fetal Brain Biometry Using 3D Slice-to-Volume MRI in Clinical Practice.

AJNR. American journal of neuroradiology·2026
Same author

Impact of gastrostomy tube placement on meningitis in infants with hydrocephalus requiring ventriculoperitoneal shunt.

Journal of perinatology : official journal of the California Perinatal Association·2026
Same author

Procedural Sedation outside the Operating Room in Pediatric Hematopoietic Stem Cell Transplant Patients.

Pediatric blood & cancer·2026
Same author

First-in-child phase I trial of p-STAT3 inhibitor WP1066 in pediatric brain tumor patients.

JCI insight·2025
Same journal

Predictor-Assisted Nonparametric Graphical Models With Multivariate Error-Prone Data.

Statistics in medicine·2026
Same journal

Optimizing Treatment Decision Estimation for Right-Censored Survival Data Through Parameter Transfer Learning.

Statistics in medicine·2026
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
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
See all related articles

Related Experiment Video

Updated: Jun 4, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Adjusting for confounding by neighborhood using complex survey data.

Babette A Brumback1, Zhulin He

  • 1Department of Biostatistics, University of Florida, Gainesville, FL 32611, USA. brumback@ufl.edu

Statistics in Medicine
|February 3, 2011
PubMed
Summary
This summary is machine-generated.

New methods for analyzing complex survey data improve neighborhood confounding adjustment. This approach, using weighted logistic regression, successfully estimates exposure effects even when previous methods failed, offering broader applicability.

Related Experiment Videos

Last Updated: Jun 4, 2026

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

Area of Science:

  • Statistics
  • Epidemiology
  • Survey Methodology

Background:

  • Adjusting for neighborhood confounding in complex survey data is challenging, especially with small neighborhood sample sizes and informative selection bias.
  • Previous adaptations of genetic methods offer consistent estimators but can be complex to implement.

Purpose of the Study:

  • To simplify and broaden the application of a weighted pseudolikelihood method for neighborhood confounding adjustment in complex survey data.
  • To demonstrate the efficacy of a new, simplified implementation compared to existing methods.

Main Methods:

  • Translated a pairwise pseudo-conditional likelihood into an equivalent ordinary weighted log-likelihood formulation.
  • Utilized standard software for ordinary logistic regression with complex survey data (e.g., SAS PROC SURVEYLOGISTIC).
  • Extended the methodology to a wider range of sampling scenarios.

Main Results:

  • The simplified method successfully adjusted for neighborhood confounding in simulations where previous methods failed.
  • The new approach demonstrated robust performance in estimating exposure effects.
  • Applied to National Health Interview Survey (NHIS) data, it estimated the education-health insurance coverage relationship, adjusting for neighborhood factors.

Conclusions:

  • A simplified weighted log-likelihood approach provides a practical and effective solution for neighborhood confounding in complex survey data.
  • This method is more broadly applicable and performs well, even in challenging scenarios.
  • The technique is programmable using readily available statistical software.