Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Strategies for Assessing and Addressing Confounding

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 Studies

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...

Longitudinal Studies

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...

Cross-Sectional Research

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...

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

You might also read

Related Articles

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

Sort by

Same author

Death or survival, which you measure may affect conclusions: A methodological study.

Health science reports·2022

Same author

Fidelity of a Traffic Safety Education Intervention for Combat Veterans.

Occupational therapy in health care·2021

Same author

Use of negative controls in a prescription sequence symmetry analysis to reduce time-varying bias.

Pharmacoepidemiology and drug safety·2021

Same author

US national trends in bariatric surgery: A decade of study.

Surgery·2021

Same author

Impact of an Emergency Department-to-Home Transitional Care Intervention on Health Service Use in Medicare Beneficiaries: A Mixed Methods Study.

Medical care·2020

Same author

Audiovisual Modules to Enhance Informed Consent in the ICU: A Pilot Study.

Critical care explorations·2020

Search research articles

Related Experiment Video

Updated: May 18, 2026

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

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

Published on: July 3, 2020

Adjusting for confounding by neighborhood using generalized linear mixed models and complex survey data.

Babette A Brumback¹, Hao W Zheng, Amy B Dailey

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

Statistics in Medicine

|September 15, 2012

Summary

New methods using generalized linear mixed models (GLMMs) help adjust for neighborhood-level factors in health disparity research. This approach, applied to dental cleaning data, yields results similar to previous methods.

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Related Experiment Videos

Last Updated: May 18, 2026

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

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

Published on: July 3, 2020

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Area of Science:

Epidemiology
Biostatistics
Health Services Research

Background:

Investigating health disparities requires understanding how neighborhood socioeconomic factors influence individual health outcomes.
Previous methods adjusted for unmeasured neighborhood confounders using conditional likelihood.
Generalized linear mixed models (GLMMs) offer a popular alternative for complex survey data analysis.

Purpose of the Study:

To propose and evaluate a new adaptation of GLMMs for complex survey data.
To adjust for confounding by unmeasured neighborhood-level covariates.
To investigate health disparities in dental cleaning recency.

Main Methods:

Developed a new GLMM approach for complex survey data to adjust for unmeasured neighborhood confounders.
Required correct modeling of the unmeasured neighborhood-level effect.
Utilized simulations to assess estimation consistency.
Applied methods to the 2008 Florida Behavioral Risk Factor Surveillance System (BRFSS) survey data, merging with census data.

Main Results:

Simulations showed that census data on individual-level covariates may be needed for consistent estimation.
The new GLMM approach was applied to analyze disparities in dental cleaning recency.
Neighborhood was defined by zip code, integrating BRFSS and census data.
Results were qualitatively similar to previous conditional likelihood analyses.

Conclusions:

The proposed GLMM adaptation is a viable method for adjusting for neighborhood-level confounding in health disparity studies.
Accurate modeling of neighborhood effects and potential use of census data are crucial for reliable estimation.
The findings support the utility of GLMMs in epidemiological research with complex survey designs.