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

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

310
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...
310
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.8K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.8K
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

9.3K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
9.3K
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

1.3K
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...
1.3K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

299
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...
299
Multiple Regression01:25

Multiple Regression

4.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Identifying patient-centered outcomes in progressive familial intrahepatic cholestasis: Results from IMPACT.

Journal of pediatric gastroenterology and nutrition·2026
Same author

Clinician Perspectives on the Implementation of a Web-Based Tool to Support Shared Decision-Making for Mid-Adult HPV Vaccination.

Journal of cancer education : the official journal of the American Association for Cancer Education·2026
Same author

Baseline analysis of patient reported outcomes in the progressive familial intrahepatic cholestasis patient registry.

Journal of pediatric gastroenterology and nutrition·2026
Same author

Implementation of mid-adult HPV vaccination guidelines into clinical practice.

Vaccine·2025
Same author

Testing an HPV Vaccine Decision Aid for 27- to 45-Year-Old Adults in the United States: A Randomized Trial.

Medical decision making : an international journal of the Society for Medical Decision Making·2024
Same author

Demographic disparities in the limited awareness of alcohol use as a breast cancer risk factor: empirical findings from a cross-sectional study of U.S. women.

BMC public health·2024
Same journal

The impact of the Memory Support Intervention on therapist memory for treatment contents.

Behaviour research and therapy·2026
Same journal

Dismantling the mechanism of VR self-compassion training: A two-session controlled trial with active controls.

Behaviour research and therapy·2026
Same journal

Supporting children on therapy waitlists: A randomized controlled trial of a web-based parent-focused single session intervention for child anxiety.

Behaviour research and therapy·2026
Same journal

Examining the roles of biased expectancies and weighting of valenced information in trait anxiety-linked state affect when approaching potentially stressful future events.

Behaviour research and therapy·2026
Same journal

Problem-solving therapy versus supportive psychotherapy for Veterans with moderate suicide risk and chronic pain: A pilot randomized clinical trial.

Behaviour research and therapy·2026
Same journal

A meta-analysis of cognitive behavioral therapy for substance use disorder: Treatment effects by comparator type and consumption and psychosocial outcomes.

Behaviour research and therapy·2026
See all related articles

Related Experiment Video

Updated: Mar 3, 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

Fitting latent variable mixture models.

Gitta H Lubke1, Justin Luningham1

  • 1Department of Psychology, University of Notre Dame, Notre Dame, IN, United States.

Behaviour Research and Therapy
|May 3, 2017
PubMed
Summary
This summary is machine-generated.

Latent variable mixture models (LVMMs) help understand complex data from diverse groups by separating continuous traits from categorical subtypes. This research details their theory and practical application using growth mixture modeling.

Keywords:
Growth mixture modelsLatent class analysisMixture modeling

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

6.1K

Related Experiment Videos

Last Updated: Mar 3, 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
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.3K
A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
13:54

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

Published on: August 18, 2023

6.1K

Area of Science:

  • Statistics
  • Psychometrics
  • Data Analysis

Background:

  • Latent variable mixture models (LVMMs) analyze multivariate data from heterogeneous populations.
  • Observed variables are influenced by latent continuous factors (e.g., disorder severity) and/or categorical variables (e.g., disorder subtypes).
  • Decomposing covariances into group membership and trait effects is complex.

Purpose of the Study:

  • To provide the theoretical background of LVMMs.
  • To outline the general framework, assumptions, and constraints for LVMMs.
  • To discuss practical issues in fitting LVMMs, including measurement invariance and model complexity.

Main Methods:

  • Theoretical exposition of LVMMs.
  • Exploration of models with and without covariates.
  • Illustrative example using growth mixture modeling with simulated data.

Main Results:

  • The paper clarifies the exploratory nature of LVMMs.
  • It highlights the interrelation between the number of classes and within-class model complexity.
  • Practical fitting issues and measurement invariance are discussed.

Conclusions:

  • LVMMs offer a robust framework for analyzing complex population structures.
  • Understanding theoretical underpinnings and practical considerations is crucial for effective application.
  • Growth mixture modeling provides a practical approach to implementing LVMMs.