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

A measurement error model with a Poisson distributed surrogate.

Liang Li1, Mari Palta, Jun Shao

  • 1Department of Biostatistics and Epidemiology/Wb4, Cleveland Clinic Foundation, Cleveland, OH 44195, USA. lli@bio.ri.ccf.org

Statistics in Medicine
|August 3, 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

Pinocembrin Alleviates Gingival Fibroblast Senescence in a Mouse Model of Periodontitis Via CYP1B1 Downregulation.

International dental journal·2026
Same author

Deciphering the Functional Mechanisms of eIF3f in Tumors and Exploring Targeted Therapies.

Pharmacology research & perspectives·2026
Same author

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same author

Inhibition of NHE1 induces trophoblast cell senescence via ADGRG6 mediated by CXCL8.

Journal of reproductive immunology·2026
Same author

Association Between Polymorphisms of 4 Common Genes and High Myopia Risk: A Comprehensive Analysis.

Medical science monitor : international medical journal of experimental and clinical research·2026
Same author

Neuroprotective effects of traditional Chinese medicine using zebrafish models - a review.

Frontiers in pharmacology·2026
Same journal

Predictor-Assisted Nonparametric Graphical Models With Multivariate Error-Prone Data.

Statistics in medicine·2026
Same journal

Optimizing Treatment Decision Estimation for Right-Censored Survival Data Through Parameter Transfer Learning.

Statistics in medicine·2026
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
See all related articles

This study introduces a new method to correct for measurement error in linear models using event counts as surrogates. The approach provides unbiased regression coefficients without needing extra data or distributional assumptions.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Linear models are widely used in statistical analysis.
  • Covariate measurement error can lead to biased results.
  • Event counts are sometimes used as surrogates for true covariates.

Purpose of the Study:

  • To develop a method for correcting bias in linear models when a covariate is measured with error.
  • To propose unbiased estimating equations using event counts as surrogates.
  • To avoid supplemental data requirements common in measurement error analyses.

Main Methods:

  • Modeling event counts using a Poisson distribution.
  • Developing unbiased estimating equations to correct for measurement error.
  • No distributional assumptions are made for the unobserved covariate.

Related Experiment Videos

Main Results:

  • Ignoring measurement error leads to inconsistent regression coefficient estimators.
  • The proposed method yields unbiased estimators.
  • The method is computationally simple and does not require supplemental data.

Conclusions:

  • The proposed method effectively corrects for measurement error in linear models.
  • This approach offers a computationally efficient and data-sparing alternative.
  • The method is applicable to real-world data, as demonstrated by the Wisconsin Sleep Cohort Study example.