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 Experiment Videos

Identifying target populations for screening or not screening using logic regression.

Holly Janes1, Margaret Pepe, Charles Kooperberg

  • 1Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. hjanes@u.washington.edu

Statistics in Medicine
|November 30, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Genetic underpinnings of the heterogeneous impact of obesity on lipid levels and cardiovascular disease.

Genome medicine·2025
Same author

Polygenic risk score for type 2 diabetes shows context-dependent effects across populations.

Nature communications·2025
Same author

Genetic architecture and analysis practices of circulating metabolites in the NHLBI Trans-Omics for Precision Medicine Program.

American journal of human genetics·2025
Same author

Genome-wide association study provides novel insight into the genetic architecture of severe obesity.

PLoS genetics·2025
Same author

Whole genome sequence analysis of low-density lipoprotein cholesterol across 246 K individuals.

Genome biology·2025
Same author

An Efficient Lasso Framework for Admixture-Aware Polygenic Scores.

bioRxiv : the preprint server for biology·2025
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Identifying combinations of colorectal cancer risk factors is challenging. This study found that current risk factors, even combined using advanced statistical methods, do not reliably predict individual risk for colorectal cancer screening.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Oncology

Background:

  • Colorectal cancer (CRC) is a major public health issue.
  • Effective CRC screening methods exist, but early detection is key.
  • Individual risk factors for CRC are not highly predictive.

Purpose of the Study:

  • To identify combinations of risk factors for colorectal cancer.
  • To determine if these combinations can stratify individuals into high or low-risk groups for targeted screening.

Main Methods:

  • Utilized data from the Colorectal Cancer Family Registry (Seattle site).
  • Employed logic regression to analyze combinations of binary risk factors.
  • Compared findings with stepwise logistic regression models.

Related Experiment Videos

Main Results:

  • Neither logic regression nor logistic regression models provided criteria to direct CRC screening.
  • The identified risk factor combinations did not effectively distinguish between cases and controls.

Conclusions:

  • Current risk factors, even when combined, are insufficient for targeted colorectal cancer screening.
  • The statistical approach may be valuable in other contexts where risk factors are more predictive.