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

611
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,...
611
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

273
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...
273
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

288
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
288
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

465
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
465
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.2K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.2K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

606
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
606

You might also read

Related Articles

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

Sort by
Same author

Using Importance Sampling to Estimate <i>p</i>-values in All-Subset Meta-Analysis, with Applications to Single-Cell eQTL Mapping.

ArXiv·2026
Same author

Postpartum depressive symptoms and stress are associated with more frequent feeding to soothe in an observational cohort study.

Appetite·2026
Same author

Semi-targeted Metabolomics Analysis of Biomarkers of Low to Moderate Alcohol Intake in the Postmenopausal Women's Alcohol Study: A Randomized Controlled Crossover Feeding Study.

The Journal of nutrition·2026
Same author

Maternal serum concentrations of persistent organic pollutants and childhood acute lymphoblastic leukemia in the Finnish Maternity Cohort.

Environmental research·2026
Same author

Plasma amino acids in early pregnancy and fetal growth trajectories across pregnancy: findings from a multi-racial U.S. pregnancy cohort.

European journal of nutrition·2026
Same author

Validation of Methylated DNA Markers for Esophageal Squamous Cancer: An International Study.

Cancer prevention research (Philadelphia, Pa.)·2026

Related Experiment Video

Updated: Feb 19, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K

A joint model for multivariate hierarchical semicontinuous data with replications.

Wondwosen Kassahun-Yimer1, Paul S Albert2, Leah M Lipsky3

  • 11 Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland, USA.

Statistical Methods in Medical Research
|November 10, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel joint model for analyzing complex longitudinal dietary data with many zero values. The approach effectively handles multivariate responses and within-subject correlations, improving understanding of episodic food consumption patterns.

Keywords:
Beta-binomialjoint modelmany zerosmultivariatesemicontinuous

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K

Related Experiment Videos

Last Updated: Feb 19, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K

Area of Science:

  • Biostatistics
  • Nutritional Epidemiology
  • Longitudinal Data Analysis

Background:

  • Longitudinal studies often involve repeated measurements on multiple response variables per subject.
  • Analyzing such data is complicated by the presence of numerous zero values, particularly in dietary intake studies of episodically consumed foods.
  • Existing models may not adequately capture the joint evolution and association of multivariate longitudinal outcomes with excess zeros.

Purpose of the Study:

  • To propose a joint statistical model for analyzing multivariate hierarchical semicontinuous longitudinal data.
  • To specifically address the challenge of numerous zero observations in dietary intake data.
  • To model the distinct probability mechanisms for zero consumption versus mean intake when consumption occurs.

Main Methods:

  • Development of a joint model for multivariate, semicontinuous longitudinal data.
  • Incorporation of separate models for zero-inflation and the distribution of non-zero values.
  • Application of a pairwise model fitting approach suitable for a large number of multivariate profiles.
  • Accounting for within-subject correlation, overdispersion, and replicate measurements.

Main Results:

  • The proposed model successfully analyzes multivariate longitudinal data with a high proportion of zero values.
  • It differentiates between the processes generating zero and non-zero consumption.
  • The pairwise fitting approach is effective for large-scale multivariate analyses.

Conclusions:

  • The novel joint model provides a robust framework for analyzing complex dietary intake data with excess zeros.
  • This approach enhances the understanding of joint evolution and associations in multivariate longitudinal studies.
  • The method is applicable to other biomedical fields with similar data structures.