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

Orthogonal Trajectories01:26

Orthogonal Trajectories

274
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
274
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

723
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,...
723
Quadratic Models01:23

Quadratic Models

358
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
358
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.8K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
2.8K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

323
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...
323
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

134
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
134

You might also read

Related Articles

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

Sort by
Same author

Measuring criticism of the police in the local news media using large language models.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Recent Advances in Group-Based Trajectory Modeling for Clinical Research.

Annual review of clinical psychology·2024
Same author

Cohort bias in predictive risk assessments of future criminal justice system involvement.

Proceedings of the National Academy of Sciences of the United States of America·2023
Same author

Mortality Risk in Pediatric Sepsis Based on C-reactive Protein and Ferritin Levels.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies·2022
Same author

Bayesian Outcome Prediction After Resuscitation From Cardiac Arrest.

Neurology·2022
Same author

The future of human behaviour research.

Nature human behaviour·2022
Same journal

Trends in the Use of Anamorelin Hydrochloride for Cancer Cachexia: Examining the Timing of Anamorelin Initiation.

Annals of nutrition & metabolism·2026
Same journal

Taurine and metabolic disorders: from mechanisms to clinical implications.

Annals of nutrition & metabolism·2026
Same journal

Maternal Nutritional Status and Breastmilk Composition.

Annals of nutrition & metabolism·2026
Same journal

Inflammation and Chronic Disease: The Mediterranean Diet in Precision and Personalized Nutrition.

Annals of nutrition & metabolism·2026
Same journal

Electromagnetic-guided nasogastric tube insertion by nurses: a multicenter non-inferiority study.

Annals of nutrition & metabolism·2026
Same journal

Patient acceptability of partial enteral nutrition as a concomitant therapy to Adalimumab in adults with active Crohn's disease - BIOPIC trial.

Annals of nutrition & metabolism·2026
See all related articles

Related Experiment Video

Updated: Apr 20, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.3K

Group-based trajectory modeling: an overview.

Daniel S Nagin1

  • 1Carnegie Mellon University, Pittsburgh, Pa., USA.

Annals of Nutrition & Metabolism
|November 22, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a group-based statistical method for analyzing developmental trajectories, offering clear graphical and tabular summaries. This approach enhances understanding of development predictors and outcomes for diverse audiences.

More Related Videos

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.9K
Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

10.5K

Related Experiment Videos

Last Updated: Apr 20, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.3K
Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.9K
Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

10.5K

Area of Science:

  • Statistics
  • Developmental Science
  • Data Analysis

Background:

  • Analyzing developmental trajectories (changes over time) is crucial in many scientific fields.
  • Existing methods may lack accessibility for diverse audiences.
  • A need exists for clear, visualizable statistical tools for developmental data.

Purpose of the Study:

  • To present a group-based statistical methodology for analyzing developmental trajectories.
  • To demonstrate the method's utility in creating accessible data summaries.
  • To provide a tool for understanding predictors and consequences of developmental paths.

Main Methods:

  • Overview of a group-based statistical methodology.
  • Focus on generating graphical and tabular data summaries.
  • Application examples illustrating the method's use.

Main Results:

  • The method produces easily understood graphical and tabular data summaries.
  • These summaries effectively portray predictors and consequences of distinct developmental trajectories.
  • Findings are accessible to both nontechnical and technically sophisticated audiences.

Conclusions:

  • The group-based statistical method offers a powerful tool for analyzing and presenting developmental trajectories.
  • Its strength lies in creating comprehensible data summaries for broad scientific communication.
  • Facilitates a deeper statistical understanding of developmental processes and influencing factors.