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

Analysis of ulcer data using hierarchical generalized linear models.

Youngjo Lee1, John A Nelder

  • 1Department of Statistics, Seoul National University, Seoul, Korea. youngjo@plaza.snu.ac.kr

Statistics in Medicine
|January 10, 2002
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

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026
Same author

Overcoming Dilution of Collision Probability in Satellite Conjunction Analysis via Confidence Distribution.

Entropy (Basel, Switzerland)·2025
Same author

Development of a Risk Tracking Model for Neurological Deterioration in Ischemic Stroke Based on Blood Pressure Dynamics.

Journal of the American Heart Association·2025
Same author

Content Swapping: A New Image Synthesis for Construction Sign Detection in Autonomous Vehicles.

Sensors (Basel, Switzerland)·2022
Same author

Semiparametric estimation for nonparametric frailty models using nonparametric maximum likelihood approach.

Statistical methods in medical research·2021
Same author

Penalized variable selection for cause-specific hazard frailty models with clustered competing-risks data.

Statistics in medicine·2021

Multi-centre clinical trials often show varied hospital treatment effects. Hierarchical generalized linear models and profile likelihoods can estimate these effects and overall treatment impact, accounting for hospital differences.

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Health Services Research

Background:

  • Multi-centre clinical trials exhibit variations in individual hospital treatment effects.
  • Estimating these heterogeneities is crucial for accurate treatment effect assessment.
  • Understanding hospital-specific contributions requires robust statistical methods.

Purpose of the Study:

  • To model heterogeneities in individual hospital treatment effects within multi-centre trials.
  • To estimate individual hospital treatment effects and the overall mean treatment effect.
  • To quantify the uncertainty associated with these estimates, considering hospital differences.

Main Methods:

  • Utilizing hierarchical generalized linear models for systematic inference.

Related Experiment Videos

  • Applying hierarchical-likelihood methods to model random effects.
  • Constructing profile likelihoods to estimate individual hospital treatment effects.

    • Main Results:

    • Demonstrated the feasibility of systematic inferences using hierarchical generalized linear models.
    • Provided methods for estimating individual hospital treatment effects alongside the overall mean effect.
    • Showcased the construction of profile likelihoods for precise estimation.

    Conclusions:

    • Hierarchical generalized linear models offer a robust framework for analyzing multi-centre trial data.
    • Profile likelihoods enable accurate estimation of individual hospital treatment effects and their uncertainty.
    • These methods improve the understanding of treatment variations across different healthcare settings.