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

Longitudinal data analysis for discrete and continuous outcomes.

S L Zeger, K Y Liang

    Biometrics
    |March 1, 1986
    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

    Latent Transition Modeling of Progression of Health-Risk Behavior.

    Multivariate behavioral research·2016
    Same author

    Homeobox genes in obsessive-compulsive disorder.

    American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics·2011
    Same author

    Statin use and neurologic morbidity after coronary artery bypass grafting: A cohort study.

    Neurology·2009
    Same author

    A screen of SLC1A1 for OCD-related alleles.

    American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics·2009
    Same author

    The effects of DonorNet 2007 on kidney distribution equity and efficiency.

    American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons·2009
    Same author

    Meta-analysis of 32 genome-wide linkage studies of schizophrenia.

    Molecular psychiatry·2009
    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
    Same journal

    Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

    Biometrics·2026
    Same journal

    Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

    Biometrics·2026
    See all related articles

    This study introduces generalized estimating equations (GEEs) for analyzing longitudinal data, offering a robust method for understanding how covariates affect outcomes over time. The approach provides consistent estimates even with misspecified time dependence, enhancing statistical analysis reliability.

    Area of Science:

    • Biostatistics
    • Longitudinal Data Analysis
    • Statistical Modeling

    Background:

    • Longitudinal data involves repeated observations for subjects.
    • Analyzing such data requires accounting for within-subject correlations.
    • Existing methods may lack robustness to misspecified time dependence.

    Purpose of the Study:

    • To propose a unifying statistical approach for analyzing longitudinal data.
    • To develop generalized estimating equations (GEEs) for regression parameters.
    • To provide a consistent variance estimate for the proposed method.

    Main Methods:

    • Introduction of a class of generalized estimating equations (GEEs).
    • GEEs extend quasi-likelihood methods for correlated data.
    • The proposed method ensures consistent and asymptotically Gaussian solutions.

    Related Experiment Videos

    Main Results:

    • GEE solutions are consistent even if time dependence is misspecified.
    • A consistent variance estimate is presented.
    • The approach is illustrated using real-world longitudinal data.

    Conclusions:

    • The proposed GEE approach offers a flexible and robust method for longitudinal data analysis.
    • It effectively models the relationship between covariates and outcomes while handling correlation.
    • This method enhances the analysis of discrete and continuous outcomes in repeated measures studies.