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

Comparative exposure ratios: a non-parametric, multifactor technique for case-control studies

M Aickin1, C Ritenbaugh, E Surwit

  • 1Department of Family and Community Medicine, University of Arizona, Tucson 85724.

Statistics in Medicine
|February 15, 1994
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

Estimates of benefits and harms of prophylactic use of aspirin in the general population.

Annals of oncology : official journal of the European Society for Medical Oncology·2014
Same author

Habitual tea consumption and risk of osteoporosis: a prospective study in the women's health initiative observational cohort.

American journal of epidemiology·2003
Same author

Obesity prevention: the case for action.

International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity·2002
Same author

Validation of the Arizona Activity Frequency Questionnaire using doubly labeled water.

Medicine and science in sports and exercise·2001
Same author

Taking issue with breast cancer statistics.

Alternative therapies in health and medicine·2001
Same author

Environmental and societal factors affect food choice and physical activity: rationale, influences, and leverage points.

Nutrition reviews·2001
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
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
See all related articles

We introduce the comparative exposure ratio (CER), a flexible measure of association for case-control studies. CER offers advantages over the odds ratio, handling various risk factors and enabling causal pathway exploration.

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • The odds ratio is a standard measure of association in case-control studies for binary risk factors.
  • Limitations exist for odds ratios with non-binary or continuous risk factors, and for simultaneous investigation of multiple factors.

Purpose of the Study:

  • To introduce the Comparative Exposure Ratio (CER) as a more general and flexible measure of association for case-control studies.
  • To demonstrate CER's advantages over the odds ratio in handling diverse risk factor types and enabling advanced analyses.

Main Methods:

  • The Comparative Exposure Ratio (CER) is defined as the ratio of case-control pairs with greater exposure in cases versus controls.
  • CER accommodates binary and continuous risk factors without requiring scale selection.
  • Methods for simultaneous investigation of multiple risk factors and confidence interval calculations for CERs are presented.

Related Experiment Videos

Main Results:

  • CER generalizes the odds ratio and remains valid when odds ratio computation is infeasible.
  • CER allows for simultaneous analysis of multiple risk factors without parametric assumptions.
  • Pilot simulations confirm the validity of confidence intervals for CERs.

Conclusions:

  • The Comparative Exposure Ratio (CER) is a powerful and flexible tool for measuring association in case-control studies.
  • CER facilitates the detection of patterns indicative of potential causal pathways.
  • Exploratory inference using CERs is demonstrated with a case-control study of cervical dysplasia.