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

Adjustment for non-differential misclassification error in the generalized linear model.

X H Liu1, K Y Liang

  • 1Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205.

Statistics in Medicine
|August 1, 1991
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

[Distribution characteristics of depression symptoms and influencing factors in perimenopausal women].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2026
Same author

[Clinical characteristics of juvenile dermatomyositis in anti-nuclear matrix protein 2 antibody-positive patients and risk factors for severity: a national multicenter retrospective study].

Zhonghua er ke za zhi = Chinese journal of pediatrics·2025
Same author

[Analysis of common non-bacterial pathogens in hospitalized children with acute respiratory infections: a multicenter study in four regions of Fujian Province in 2023].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2025
Same author

[Efficacy of ruxolitinib and prognostic factors in patients with myelofibrosis stratified by age].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi·2025
Same author

[Current research and future perspectives on oropouche virus].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2025
Same author

[Comparison of magnetic resonance images of the temporomandibular joint using different coils].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology·2025
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

Estimating associations with misclassified covariates biases results. This study introduces a method using latent class analysis and multiple surrogates to correct bias in generalized linear models, improving covariate accuracy.

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Epidemiology

Background:

  • Standard statistical methods yield biased estimates when categorical covariates are measured with misclassification error.
  • Ignoring covariate misclassification can lead to inaccurate conclusions in association studies.

Purpose of the Study:

  • To propose and demonstrate a novel method for adjusting covariate misclassification within generalized linear models.
  • To incorporate multiple surrogate measurements to improve the accuracy of covariate data.

Main Methods:

  • A latent class analysis approach is combined with methods for incorporating multiple surrogates.
  • The proposed method adjusts for misclassification in covariates within a regression framework.
  • The efficacy of repeated measurements as a special case of multiple surrogates is discussed.

Related Experiment Videos

Main Results:

  • The developed method effectively adjusts for misclassification bias in covariate estimation.
  • Demonstrated applicability through two real-world examples.
  • Highlighted the utility of multiple replicates for improving accuracy of misclassified covariates.

Conclusions:

  • The proposed method provides a robust approach to handle misclassified covariates in generalized linear models.
  • Utilizing multiple surrogates, including repeated measurements, significantly enhances the reliability of regression analyses.
  • This technique is crucial for obtaining unbiased estimates in epidemiological and biostatistical research.