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

Joint models for multivariate longitudinal and multivariate survival data.

Yueh-Yun Chi1, Joseph G Ibrahim

  • 1Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, USA. yychi@bios.unc.edu

Biometrics
|August 22, 2006
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

Ixazomib with chemotherapy for childhood relapsed acute lymphoblastic leukemia: a TACL consortium report.

Blood neoplasia·2026
Same author

A novel maternal prenatal risk index to predict mortality-weighted severe maternal morbidity at hospitalization: a retrospective cohort study.

Lancet regional health. Americas·2026
Same author

Information-Based Composite Likelihood Method for Hybrid Meta-Analysis Integrating Individual Participant Data and Aggregated Data.

Statistics in medicine·2026
Same author

Canopy2: Tumor Phylogeny Inference by Bulk DNA and Single-Cell RNA Sequencing.

Statistics in biosciences·2026
Same author

Mortality-weighted severe maternal morbidity: a novel approach to assessing maternal health outcomes.

BMC pregnancy and childbirth·2025
Same author

Pair-Feeding Study Designs Can Create Biases and Inflate Type I Error Rates: A Simulation Study.

Obesity (Silver Spring, Md.)·2025
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 a new statistical approach for joint modeling of longitudinal and survival data, crucial for cancer and AIDS clinical trials. The method enhances understanding of disease progression and treatment outcomes by analyzing multiple data types simultaneously.

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Survival Analysis

Background:

  • Joint modeling of longitudinal and survival data is vital in cancer and AIDS research.
  • Existing models often lack the flexibility to handle multidimensional data and complex dependencies.

Purpose of the Study:

  • To develop a flexible statistical framework for joint modeling of multidimensional longitudinal and survival data.
  • To account for various sources of dependence within longitudinal measures and between failure times.
  • To accommodate survival functions with different cure fraction structures.

Main Methods:

  • A likelihood-based approach using a multivariate mixed-effects model for longitudinal data.
  • Incorporation of a shared frailty model with a positive stable distribution for survival data.

Related Experiment Videos

  • Application of marginal univariate survival models with flexible cure fractions.
  • Development of a multivariate survival model with proportional hazards structure.

    • Main Results:

    • The proposed model effectively captures complex dependencies in longitudinal and survival data.
    • It allows for the analysis of survival data with both zero and non-zero cure fractions.
    • The methodology was successfully applied to the International Breast Cancer Study Group (IBCSG) trial.

    Conclusions:

    • The developed joint modeling framework offers a robust method for analyzing complex clinical trial data.
    • It provides deeper insights into the relationships between quality of life, disease-free survival, and overall survival.
    • This approach is essential for advancing statistical methods in cancer and AIDS research.