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

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 squares (OLS)...
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

You might also read

Related Articles

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

Sort by
Same author

Enhancing vaccine safety surveillance: a capture-recapture analysis of intussusception after rotavirus vaccination.

American journal of epidemiology·2001
Same author

Vaccination practices, policies, and management factors associated with high vaccination coverage levels in Georgia public clinics. Georgia Immunization Program Evaluation Team.

Archives of pediatrics & adolescent medicine·2000
Same author

Increased susceptibility to measles in infants in the United States.

Pediatrics·1999
Same author

Re: "Invited commentary: What can we infer from author order in epidemiology?".

American journal of epidemiology·1999
Same author

Impact of measurement and feedback on vaccination coverage in public clinics, 1988-1994.

JAMA·1997
Same author

Seroprevalence of antibody against poliovirus in inner-city preschool children. Implications for vaccination policy in the United States.

JAMA·1996

Related Experiment Video

Updated: Jul 11, 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

Mixture model framework facilitates understanding of zero-inflated and hurdle models for count data.

A L Baughman1

  • 1National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia 30329, USA. ALB1@cdc.gov

Journal of Biopharmaceutical Statistics
|September 22, 2007
PubMed
Summary

This note clarifies zero-inflated and hurdle models for count data by viewing them as finite mixture models. Understanding model components and latent variables aids in selecting appropriate statistical models for complex data.

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Related Experiment Videos

Last Updated: Jul 11, 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

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Area of Science:

  • Biostatistics
  • Statistical Modeling

Background:

  • Zero-inflated and hurdle models are used for count data analysis.
  • These models address excess zeros often found in biological and pharmaceutical data.
  • Understanding the underlying assumptions of these models is crucial for accurate interpretation.

Purpose of the Study:

  • To provide a commentary on the zero-inflated and hurdle models.
  • To enhance understanding by framing these models as finite mixture models.
  • To guide the selection of appropriate models through expert collaboration.

Main Methods:

  • Reinterpreting zero-inflated and hurdle models as finite mixture models.
  • Analyzing the components and latent variable assumptions within these statistical frameworks.
  • Emphasizing the role of subject matter experts in model selection.

Main Results:

  • Viewing these models as finite mixture models offers deeper insight into their structure.
  • The components and assumptions, including latent variables, become clearer.
  • This perspective facilitates a more informed choice between zero-inflated and hurdle models.

Conclusions:

  • Finite mixture modeling provides a valuable lens for understanding zero-inflated and hurdle count data models.
  • Subject matter expert collaboration is essential for appropriate model application.
  • Considerations like pharmacokinetic rationale are vital when developing mixture models for specific data, such as vaccine adverse events.