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

Statistical methods for longitudinal and clustered designs with binary responses.

J M Neuhaus1

  • 1Department of Epidemiology and Biostatistics, University of California, San Francisco 94143-0560.

Statistical Methods in Medical Research
|January 1, 1992
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

Unsupervised Online Paired Associates Learning Task from the Cambridge Neuropsychological Test Automated Battery (CANTAB®) in the Brain Health Registry.

The journal of prevention of Alzheimer's disease·2024
Same author

RAD51 loss of function abolishes gene targeting and de-represses illegitimate integration in the moss Physcomitrella patens.

DNA repair·2010
Same author

Fear conditioning in frontotemporal lobar degeneration and Alzheimer's disease.

Brain : a journal of neurology·2008
Same author

Family-specific approaches to the analysis of case-control family data.

Biometrics·2006
Same author

A comparison of lorazepam, diazepam, and placebo for the treatment of out-of-hospital status epilepticus.

The New England journal of medicine·2001
Same author

Regeneration of a lytic central vacuole and of neutral peripheral vacuoles can be visualized by green fluorescent proteins targeted to either type of vacuoles.

Plant physiology·2001
Same journal

A joint model for a longitudinal outcome and a progressive multistate model under a mixed observation scheme.

Statistical methods in medical research·2026
Same journal

Efficient semi-supervised estimation of optimal individualized treatment regimes with survival outcome.

Statistical methods in medical research·2026
Same journal

Asymptotic online FWER control for dependent test statistics.

Statistical methods in medical research·2026
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
See all related articles

Analyzing repeated binary outcome data requires careful consideration of different statistical approaches. This study compares methods, clarifying which questions each can answer and guiding analysts on their advantages and disadvantages for dependent binary responses.

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Statistical Modeling

Background:

  • Dependent binary response data are common in longitudinal studies and subsampling designs.
  • Various analytical approaches exist for repeated binary outcome data.
  • These methods differ in the effects of covariates they measure and the statistical questions they address.

Purpose of the Study:

  • To compare different classes of approaches for analyzing dependent binary response data.
  • To clarify parameter interpretation, magnitude, standard errors, and Wald tests across methods.
  • To guide data analysts in selecting appropriate methods based on their research questions.

Main Methods:

  • Comparative analysis of different statistical approaches for dependent binary data.

Related Experiment Videos

  • Evaluation of parameter interpretation, magnitude, standard errors, and Wald tests.
  • Use of simulations and example data for illustration.

    • Main Results:

    • Different approaches measure distinct covariate effects and address varied statistical questions.
    • Significant differences observed in parameter interpretation, magnitude, and statistical test outputs.
    • The choice of method impacts the substantive conclusions drawn from the data.

    Conclusions:

    • Understanding the differences between analytical approaches is crucial for accurate interpretation of covariate effects.
    • Provides guidelines on the advantages and disadvantages of alternative methods for dependent binary responses.
    • Aids data analysts in selecting the most suitable statistical approach for their specific research context.