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

Confounding and misclassification.

S Greenland, J M Robins

    American Journal of Epidemiology
    |September 1, 1985
    PubMed
    Summary
    This summary is machine-generated.

    Controlling for covariates can worsen bias when misclassification is present, contrary to standard confounding control methods. Adjusting for covariates requires considering the degree of misclassification to avoid misleading results.

    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

    On the asymptotic validity of confidence sets for linear functionals of solutions to integral equations.

    Biometrika·2025
    Same author

    Using negative controls to identify causal effects with invalid instrumental variables.

    Biometrika·2025
    Same author

    Multiple robustness in factorized likelihood models.

    Biometrika·2018
    Same author

    Instrumental variables as bias amplifiers with general outcome and confounding.

    Biometrika·2017
    Same author

    Marginal Mean Models for Dynamic Regimes.

    Journal of the American Statistical Association·2009
    Same author

    Glaucoma outcome studies using existing databases: opportunities and limitations.

    Journal of glaucoma·2009
    Same journal

    Correction to: Home dampness and molds and occurrence of respiratory tract infections in the first 27 years of life: the Espoo Cohort Study.

    American journal of epidemiology·2026
    Same journal

    A SIMPLE AND POWERFUL TEST OF VACCINE WANING.

    American journal of epidemiology·2026
    Same journal

    Association Between maternal body mass index, offspring growth and pubertal timing: results from a longitudinal birth cohort study.

    American journal of epidemiology·2026
    Same journal

    Correction to: Developing a novel algorithm to identify incident and prevalent dementia in Medicare claims-the ARIC Study.

    American journal of epidemiology·2026
    Same journal

    RE: advancing observational research on arts and health: theory-informed approaches using the RADIANCE framework.

    American journal of epidemiology·2026
    Same journal

    Maternal Cesarean Section and Offspring ASD or ADHD Risk: A Nurses' Health Study II Analysis.

    American journal of epidemiology·2026
    See all related articles

    Area of Science:

    • Epidemiology
    • Biostatistics

    Background:

    • Covariate adjustment is standard for controlling confounding in observational studies.
    • Misclassification of variables can introduce bias into study results.
    • Existing criteria for covariate adjustment may not adequately address misclassification bias.

    Purpose of the Study:

    • To evaluate the validity of recently proposed criteria for covariate adjustment in the presence of misclassification.
    • To investigate the impact of covariate control on bias when outcome or exposure misclassification occurs.
    • To determine if standard confounding control methods are appropriate for misclassification bias.

    Main Methods:

    • The study examines theoretical criteria for covariate adjustment.
    • Illustrative examples are used to demonstrate the effects of misclassification on bias.

    Related Experiment Videos

  • The authors analyze the 'change-in-estimate' criterion under misclassification.

    • Main Results:

    • Recently proposed criteria for covariate adjustment in misclassified data are shown to be incorrect.
    • Covariate control can paradoxically increase bias when misclassification is present.
    • The 'change-in-estimate' method for covariate selection is found to be misleading with misclassification.

    Conclusions:

    • Standard methods for controlling confounding are insufficient for addressing bias due to misclassification.
    • The degree of misclassification must be considered when deciding on covariate adjustment.
    • Misclassification necessitates a re-evaluation of covariate control strategies in epidemiological studies.