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

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:

You might also read

Related Articles

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

Sort by
Same author

Incidence and remission of endometriosis in Germany based on prevalence data from 35 million patients from the statutory health insurance.

BMC women's health·2026
Same author

DEVELOPMENT AND APPLICATION OF BRAIN TISSUE BASED MULTI-OMICS PROFILE SCORES FOR ALZHEIMER'S DISEASE.

Research square·2026
Same author

A review of machine learning in toxicology: current practices and reporting gaps.

Archives of toxicology·2026
Same author

Exposome-wide patterns predict brain health in aging.

Nature communications·2026
Same author

A nonparametric statistical method for deconvolving densities in the analysis of proteomic data.

BMC bioinformatics·2026
Same author

Exploring the relative contribution of genetic and external exposomic risk scores to allergies in elderly women.

Scientific reports·2026

Related Experiment Video

Updated: Jul 4, 2026

Therapy Testing in a Spheroid-based 3D Cell Culture Model for Head and Neck Squamous Cell Carcinoma
06:11

Therapy Testing in a Spheroid-based 3D Cell Culture Model for Head and Neck Squamous Cell Carcinoma

Published on: April 20, 2018

Statistical methods for detecting genetic interactions: a head and neck squamous-cell cancer study.

Katja Ickstadt1, Martin Schäfer, Arno Fritsch

  • 1Fakultät Statistik, Collaborative Research Centre 475, Technische Universität Dortmund, Dortmund. ickstadt@statistik.uni-dortmund.de

Journal of Toxicology and Environmental Health. Part A
|June 24, 2008
PubMed
Summary

New statistical methods identified key genetic interactions contributing to head and neck squamous-cell cancer (HNSCC). These findings highlight the roles of specific gene variants and tobacco smoke in HNSCC development.

More Related Videos

Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis
06:57

Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis

Published on: June 14, 2019

A Model for Perineural Invasion in Head and Neck Squamous Cell Carcinoma
08:59

A Model for Perineural Invasion in Head and Neck Squamous Cell Carcinoma

Published on: January 5, 2017

Related Experiment Videos

Last Updated: Jul 4, 2026

Therapy Testing in a Spheroid-based 3D Cell Culture Model for Head and Neck Squamous Cell Carcinoma
06:11

Therapy Testing in a Spheroid-based 3D Cell Culture Model for Head and Neck Squamous Cell Carcinoma

Published on: April 20, 2018

Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis
06:57

Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis

Published on: June 14, 2019

A Model for Perineural Invasion in Head and Neck Squamous Cell Carcinoma
08:59

A Model for Perineural Invasion in Head and Neck Squamous Cell Carcinoma

Published on: January 5, 2017

Area of Science:

  • Oncology
  • Genetics
  • Environmental Health

Background:

  • Head and neck squamous-cell cancer (HNSCC) is significantly linked to tobacco smoke and polycyclic aromatic hydrocarbon (PAH) exposure.
  • Understanding the complex genetic factors and their interactions is crucial for HNSCC etiology.

Purpose of the Study:

  • To apply novel statistical methods for detecting higher-order genetic interactions in HNSCC.
  • To identify single-nucleotide polymorphisms (SNPs) and their interactions influencing HNSCC susceptibility.

Main Methods:

  • Utilized unsupervised learning (cluster analysis) and supervised learning (logic regression, Bayesian generalization).
  • Examined SNPs in PAH metabolizing/repair enzymes, somatic p53 mutations, and tobacco smoke exposure in 312 HNSCC cases and 300 controls.
  • Employed advanced statistical tools to analyze SNP patterns and interactions.

Main Results:

  • Detected significant interactions involving CYP1B1, tobacco smoke, and p53 mutations.
  • Identified interactions between CYP1B1 and glutathione S-transferases in smokers.
  • Uncovered a three-way interaction between CYP1B1, CYP2E1-70G>T, and GSTP1 (exon 5) in relation to HNSCC risk.

Conclusions:

  • Novel statistical approaches effectively identified complex genetic interactions in HNSCC.
  • Specific gene-environment interactions, particularly involving CYP1B1 and smoking, are critical in HNSCC development.
  • These findings offer insights into HNSCC pathogenesis and potential targets for risk assessment.