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

Marginal and dynamic regression models for longitudinal data.

C H Schmid1

  • 1Biostatistics Research Center, New England Medical Center and Tufts University School of Medicine, 750 Washington St, NEMC #063, Boston, MA 02111, USA. cschmid@lifespan.org

Statistics in Medicine
|December 18, 2001
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

Therapeutic trajectory following intra-articular hyaluronic acid injection in knee osteoarthritis--meta-analysis.

Osteoarthritis and cartilage·2011
Same author

Re: A randomized, placebo-controlled trial of repeated IV antibiotic therapy for Lyme encephalopathy. Prolonged Lyme disease treatment: enough is enough.

Neurology·2009
Same author

Maternal obesity and risk of cesarean delivery: a meta-analysis.

Obesity reviews : an official journal of the International Association for the Study of Obesity·2007
Same author

Cumulative illness severity and progression from moderate to severe retinopathy of prematurity.

Journal of perinatology : official journal of the California Perinatal Association·2007
Same author

Long-term non-pharmacologic weight loss interventions for adults with type 2 diabetes.

The Cochrane database of systematic reviews·2005
Same author

Long-term non-pharmacological weight loss interventions for adults with prediabetes.

The Cochrane database of systematic reviews·2005
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 compares dynamic regression models with marginal models for longitudinal data analysis. Dynamic models, using lagged responses, offer a different interpretation of regression parameters, focusing on changes in response levels.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Longitudinal Data Analysis

Background:

  • Longitudinal data analysis commonly uses random effects and serial correlation to model within- and between-subject variability.
  • Marginal models are prevalent, treating mean and covariance parameters separately.
  • Dynamic models offer an alternative by incorporating lagged responses as predictors.

Purpose of the Study:

  • To compare dynamic regression models with traditional marginal models for longitudinal data.
  • To explore the interpretation of regression parameters in dynamic models.
  • To investigate the utility of lagged predictors in quantifying the impact of prior risk factor levels.

Main Methods:

  • Utilized dynamic regression models incorporating a lagged response variable.

Related Experiment Videos

  • Compared dynamic models to marginal models for longitudinal data.
  • Applied models to analyze lung function (FEV(1)) data from the Childhood Respiratory Study over five years.

    • Main Results:

    • Regression parameters in dynamic models represent changes in response levels, differing from marginal model interpretations.
    • Lagged predictors effectively quantify the influence of previous risk factor exposures.
    • The study analyzed lung function trajectories in children based on age, height, sex, and smoking status.

    Conclusions:

    • Dynamic models provide a valuable framework for analyzing longitudinal data, particularly when examining response changes over time.
    • The inclusion of lagged variables enhances the understanding of temporal dependencies and risk factor effects.
    • The Childhood Respiratory Study data demonstrated the practical application of these modeling approaches.