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

Multivariate longitudinal models for complex change processes.

L A Beckett1, D J Tancredi, R S Wilson

  • 1Division of Biostatistics, Department of Epidemiology and Preventive Medicine, University of California, Davis, CA 95616, USA. labeckett@ucdavis.edu

Statistics in Medicine
|January 13, 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

Harmonization of late-life participation in cognitively stimulating activities across four cohort studies of cognitive aging.

Experimental gerontology·2026
Same author

Long-lasting memory of jasmonic acid-dependent immunity requires DNA demethylation and ARGONAUTE1.

Nature plants·2023
Same author

Straight to test reduces time to investigation and treatment.

The Ulster medical journal·2022
Same author

Reframing climate change assessments around risk: recommendations for the US National Climate Assessment.

Environmental research letters : ERL [Web site]·2022
Same author

Editorial: How Will Aducanumab Approval Impact AD Research?

The journal of prevention of Alzheimer's disease·2021
Same author

Developing a framework for arts in health programs targeting individuals with chronic pain: a mixed-methods study of practitioners.

Public health·2021
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
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

This study introduces advanced statistical models for analyzing longitudinal data with multiple outcomes. These methods better capture complex growth and disease progression compared to traditional single-outcome analyses.

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Epidemiology

Background:

  • Longitudinal studies are crucial for understanding growth and chronic disease progression.
  • Current analyses often focus on single outcome variables, potentially missing complex interrelationships.
  • Growth and disease involve multiple markers, necessitating multivariate approaches.

Purpose of the Study:

  • To develop and evaluate statistical models for analyzing the association between trajectories of two outcome variables over time.
  • To compare the properties of these models with traditional separate ordinary least-squares (OLS) analyses.
  • To illustrate the application of these methods using real-world longitudinal cohort data.

Main Methods:

  • Utilized random effects models to jointly analyze the trajectories of two outcome measures.

Related Experiment Videos

  • Compared the performance and insights gained from the proposed models against separate OLS regression analyses.
  • Employed data from the Religious Orders Study, a long-term cohort study with repeated clinical examinations.

    • Main Results:

    • Random effects models provide a more comprehensive understanding of the relationship between multiple outcome trajectories over time.
    • These multivariate approaches offer advantages over analyzing single outcomes independently.
    • The study demonstrates the practical application and benefits of these advanced statistical techniques.

    Conclusions:

    • Multivariate longitudinal models are essential for accurately characterizing complex biological processes involving multiple outcome measures.
    • The proposed random effects models offer a robust framework for analyzing associated changes over time.
    • These methods enhance the analytical power of longitudinal studies in fields like aging and chronic disease research.