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

Modeling longitudinal data with ordinal response by varying coefficients.

G Kauermann1

  • 1Ludwig-Maximilians-University Munich, Institute for Statistics, Germany. kauerman@stat.uni-muenchen.de

Biometrics
|September 14, 2000
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

Standard errors for EM estimates in generalized linear models with random effects.

Biometrics·2000
Same journal

Interim analysis in sequential multiple assignment randomized trials for survival outcomes.

Biometrics·2026
Same journal

Acknowledgment of Referees 2025.

Biometrics·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
See all related articles

This study introduces a novel smooth regression model for time-varying ordinal data. The model effectively captures how longitudinal dependence in ordinal observations evolves over time.

Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Ordinal data analysis presents challenges due to its discrete and ordered nature.
  • Longitudinal studies require methods that account for repeated measurements over time.
  • Understanding time-varying covariate effects is crucial in many scientific fields.

Purpose of the Study:

  • To develop a smooth regression model for ordinal data with a longitudinal dependence structure.
  • To allow main and covariate effects to vary over time.
  • To investigate the temporal evolution of longitudinal dependence in ordinal observations.

Main Methods:

  • A marginal model with a cumulative logit link function was employed for ordinal data.
  • Local fitting techniques were utilized for estimating model parameters.

Related Experiment Videos

  • Asymptotic properties of the estimators were theoretically analyzed.
  • Cumulative log odds ratios were fitted locally to model time-varying dependence.

    • Main Results:

    • The proposed smooth regression model effectively handles ordinal data with longitudinal dependence.
    • Time-varying effects of covariates and main effects were successfully incorporated.
    • The local fitting approach provided insights into how dependence structures change over time.

    Conclusions:

    • The developed model offers a flexible framework for analyzing time-dependent ordinal outcomes.
    • This methodology enhances the understanding of dynamic relationships in longitudinal ordinal data.
    • The approach is applicable to various fields dealing with repeated ordinal measurements.