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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...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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:
Hazard Ratio01:12

Hazard Ratio

The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial evaluating a...
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Confounding in Epidemiological Studies

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Related Experiment Video

Updated: May 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Matching with Multiple Criteria and Its Application to Health Disparities Research.

Chang Chen1, Zhiyu Qian2, Bo Zhang3

  • 1Biostatistics University of North Carolina at Chapel Hill.

Observational Studies
|May 11, 2026
PubMed
Summary

Statistical matching methods can reveal health disparities. This study used matching to analyze prostate-specific antigen (PSA) screening gaps between white and black men, finding disparities widen as groups become more similar.

Keywords:
Health disparitiesR packageStatistical matchingTapered matching

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Last Updated: May 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Health Services Research
  • Nonparametric Statistical Methods
  • Health Disparities Research

Background:

  • Health services research frequently employs matching for covariate adjustment.
  • The Institute of Medicine (IOM) defines health disparities as unequal access not explained by health status or patient preference.
  • Existing methods may not fully capture nuanced disparities.

Purpose of the Study:

  • To propose a statistical matching methodology for estimating health disparities consistent with the IOM definition.
  • To investigate disparities in prostate-specific antigen (PSA) screening between white and black men in the US.
  • To provide reproducible code and a tutorial for customized matched comparison group creation.

Main Methods:

  • Developed a statistical matching approach to create comparable groups.
  • Matched white men to black men on selected covariates while maintaining population identity on others.
  • Utilized the 2020 Behavioral Risk Factor Surveillance System (BRFSS) database for analysis.

Main Results:

  • Observed a widening gap in PSA screening rates as the matched white comparison group increasingly resembled the black men group.
  • The proposed methodology allows for granular examination of disparity drivers.
  • Demonstrated the application of the method to real-world health data.

Conclusions:

  • Statistical matching offers a robust strategy for quantifying health disparities.
  • The study highlights significant disparities in PSA screening access between white and black men.
  • The provided tools facilitate further research into health equity and access.