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

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

897
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:  
897
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

343
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...
343
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

666
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:
666
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.8K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
14.8K
Causality in Epidemiology01:21

Causality in Epidemiology

1.2K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.2K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

153
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
153

You might also read

Related Articles

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

Sort by
Same author

Risk Factors for Foot-Related Hospitalisations in Adults With and Without Diabetes: A Systematic Review.

Journal of foot and ankle research·2026
Same author

Dietary Patterns and Cerebral Glucose Metabolism in Older Adults: Findings from the Western Australian Memory Study.

Nutrients·2026
Same author

Spatial disparities in incidence and survival of liver cancer in Australia, 2010-2019.

Cancer epidemiology·2026
Same author

Generation of machine-learning derived cancer vulnerability indicator to determine the spatial burden of cancer outcomes.

PloS one·2026
Same author

Data resource profile: The Life and health After Childhood cancEr (LACE) project.

International journal of population data science·2026
Same author

The effect of the modifiable areal unit problem on ecological model inference: A graphical simulation study for disease mapping in Australia.

PloS one·2025
Same journal

Desert lizards modulate nutritional responses to match seasonal biological needs.

Royal Society open science·2026
Same journal

Multi-generational fidelity, ecological and social determinants of roosting in a cooperatively breeding bird (<i>Argya squamiceps</i>).

Royal Society open science·2025
Same journal

Multifaceted polarization and information reliability in climate change discussions on social media platforms.

Royal Society open science·2025
Same journal

Comparing the kinematics related to inflicted head injury between violent shaking of a 6-week-old and a 1-year-old infant surrogate.

Royal Society open science·2025
Same journal

Partner choice increases observed reciprocity-based cooperation but decreases unobserved stake-based cooperation.

Royal Society open science·2025
Same journal

Importation models for travel-related SARS-CoV-2 cases reported in Newfoundland and Labrador during the COVID-19 pandemic.

Royal Society open science·2025
See all related articles

Related Experiment Video

Updated: Nov 6, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.9K

Correction to 'Augmenting disease maps: a Bayesian meta-analysis approach'.

Farzana Jahan, Earl W Duncan, Susanna M Cramb

    Royal Society Open Science
    |May 11, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study corrects previously published article DOIs, ensuring accurate citation and referencing for scientific research. Proper citation is crucial for academic integrity and the dissemination of scientific findings.

    More Related Videos

    Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
    04:41

    Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

    Published on: January 9, 2020

    19.1K
    Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
    07:15

    Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

    Published on: January 16, 2019

    11.2K

    Related Experiment Videos

    Last Updated: Nov 6, 2025

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.9K
    Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
    04:41

    Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

    Published on: January 9, 2020

    19.1K
    Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
    07:15

    Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

    Published on: January 16, 2019

    11.2K

    Area of Science:

    • Bibliometrics
    • Scholarly Communication

    Context:

    • Accurate citation is fundamental to scientific research.
    • Digital Object Identifiers (DOIs) are essential for locating and referencing scholarly articles.

    Purpose:

    • To correct erroneous Digital Object Identifiers (DOIs) associated with a previously published article.
    • To ensure the integrity and retrievability of scientific literature.

    Summary:

    • This erratum addresses and rectifies incorrect DOIs for a specific publication.
    • The correction facilitates accurate access and citation of the research.

    Impact:

    • Enhances the reliability of scientific databases and citation records.
    • Supports the accurate tracking and impact assessment of scholarly work.
    • Upholds academic standards by ensuring correct article identification.