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 Concept Videos

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

113
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
113
Longitudinal Studies01:26

Longitudinal Studies

145
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
145
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

64
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
64
Longitudinal Research02:20

Longitudinal Research

11.9K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
11.9K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

388
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
388
Structural Classification of Joints01:20

Structural Classification of Joints

3.2K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Risk estimation and dynamic prediction using discrete-time joint models for longitudinal and multistate data with interval and state censoring.

Biostatistics (Oxford, England)·2026
Same author

Child and mother study satisfaction in a longitudinal study of children at-risk for type 1 diabetes.

BMC pediatrics·2026
Same author

Retinol, Carotenoid, and Tocopherol Intake and Status, and the Risk of Islet Autoimmunity and Type 1 Diabetes: The Environmental Determinants of Diabetes in the Young Study.

Diabetes/metabolism research and reviews·2026
Same author

Accounting for Age-Related Increases in HbA1c More Accurately Quantifies Risk of Type 1 Diabetes Progression in Islet Autoantibody-Positive Adults.

Diabetes care·2026
Same author

Heterogeneity between insulin and proinsulin in the potency for insulin autoantibodies in newly diagnosed type 1 diabetes children.

Clinical and experimental immunology·2026
Same author

Accounting for age-related increases in HbA1c more accurately quantifies risk of Type 1 Diabetes progression in islet autoantibody-positive adults.

medRxiv : the preprint server for health sciences·2026

Related Experiment Video

Updated: Jun 13, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

JOINT MODELING OF MULTISTATE AND NONPARAMETRIC MULTIVARIATE LONGITUDINAL DATA.

L U You1, Falastin Salami2, Carina Törn2

  • 1Health Informatics Institute, University of South Florida, Tampa, Florida, U.S.A.

The Annals of Applied Statistics
|September 16, 2024
PubMed
Summary

A new joint statistical model analyzes disease progression in children, offering insights into type-1 diabetes (T1D) development. This method enhances predictions for future health states in studies like The Environmental Determinants of Diabetes in the Young (TEDDY).

Keywords:
Joint modelingMultistate modelSpline regression modelType-1 diabetes

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

Related Experiment Videos

Last Updated: Jun 13, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Longitudinal Data Analysis

Background:

  • Disease progression often involves transitions between multiple health states.
  • Analyzing longitudinal data in studies like The Environmental Determinants of Diabetes in the Young (TEDDY) is crucial for understanding disease development.
  • Existing models may not fully capture the complexity of multistate and multivariate longitudinal data.

Purpose of the Study:

  • To propose a novel joint statistical model for simultaneous inference of multistate and multivariate nonparametric longitudinal data.
  • To enable statistical inference, hypothesis testing, and prediction of future disease states within the TEDDY study.
  • To address complex research questions in pediatric disease progression research.

Main Methods:

  • Development of a joint model integrating multistate and multivariate nonparametric longitudinal components.
  • Application of the model to analyze data from The Environmental Determinants of Diabetes in the Young (TEDDY) study.
  • Evaluation of the proposed method's performance through simulation studies.

Main Results:

  • The proposed joint model facilitates robust statistical inference and hypothesis testing for complex longitudinal disease data.
  • Simulation studies demonstrate the method's effectiveness and accuracy in predicting future health state occupations.
  • The model successfully applied to the TEDDY study, showcasing its practical utility in real-world research.

Conclusions:

  • The developed joint model provides a powerful tool for analyzing multistate and multivariate longitudinal data in disease progression studies.
  • This approach enhances the ability to understand and predict the trajectory of diseases like type-1 diabetes in at-risk children.
  • The method offers significant advancements for epidemiological research and clinical decision-making.