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

Odds Ratio01:09

Odds Ratio

The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
Prevalence and Incidence01:08

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health condition at a...
Statistical Methods for Analyzing Epidemiological Data01:25

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:
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...

You might also read

Related Articles

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

Sort by
Same author

Tracking of dietary patterns from early childhood to school age in the Brazilian Food and Nutritional Surveillance System, 2008-2019.

Public health nutrition·2026
Same author

Sociodemographic Factors and Childhood Growth: Associations with Environmental Sanitation Phases.

International journal of environmental research and public health·2026
Same author

Prenatal Exposure to Zika Virus and Risk of Epilepsy-Related Hospitalization During Early Childhood.

JAMA pediatrics·2025
Same author

Perinatal outcomes of symptomatic chikungunya, dengue and Zika infection during pregnancy in Brazil: a registry-based cohort study.

Nature communications·2025
Same author

Syphilis Exposure During Pregnancy and Childhood Hospital Admissions in Brazil.

JAMA network open·2025
Same author

Missed opportunities for equity: a crucial perspective on the Lancet's Zika virus research Series.

The Lancet. Infectious diseases·2025

Related Experiment Videos

Estimating adjusted prevalence ratio in clustered cross-sectional epidemiological data.

Carlos Antônio S T Santos1, Rosemeire L Fiaccone, Nelson F Oliveira

  • 1State University of Feira de Santana, Feira de Santana, Brazil. carlosateles@yahoo.com.br

BMC Medical Research Methodology
|December 18, 2008
PubMed
Summary

Odds ratios (OR) can be misleading for common outcomes in cross-sectional studies. This research presents methods for accurate prevalence ratio (PR) estimation, especially for clustered data, improving epidemiological analysis.

Related Experiment Videos

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Odds ratios (OR) are commonly used in cross-sectional studies, but can misrepresent associations with common outcomes.
  • Prevalence ratios (PR) are a more accurate measure of association for common outcomes.

Purpose of the Study:

  • To overview methods for estimating adjusted prevalence ratios.
  • To extend the discussion to clustered cross-sectional studies.
  • To compare the performance of different confidence interval estimation methods.

Main Methods:

  • Prevalence ratios (PR) estimated using logistic models with random effects.
  • Confidence intervals calculated using the delta method and clustered bootstrap.
  • Simulation studies and real-world data analysis were employed.

Main Results:

  • Significant differences observed between estimated odds ratios (OR) and prevalence ratios (PR).
  • The delta method demonstrated superior performance over bootstrap for small numbers of clusters in simulations.
  • Analysis of child health data highlighted interpretation differences.

Conclusions:

  • Logistic models with random effects are recommended for analyzing clustered data.
  • The choice between delta and bootstrap methods for confidence intervals depends on the study design.
  • Accurate estimation of prevalence ratios is crucial for valid epidemiological conclusions.