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

83
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...
83
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
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...
100
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

121
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...
121
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

686
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...
686
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

218
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
218
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

565
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...
565

You might also read

Related Articles

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

Sort by
Same author

Moral Panic and Electric Micromobilities: Seeking Space for Mobility Justice.

Sociological perspectives : SP : official publication of the Pacific Sociological Association·2026
Same author

How Is Colorectal Cancer Care Impacted by Global Crisis in Contrasting Healthcare Systems?-A Descriptive Study From Scotland and Switzerland During the COVID-19 Pandemic.

World journal of surgery·2026
Same author

Association between neoadjuvant paclitaxel dose intensity and outcomes in early triple-negative and HER2-positive breast cancer: a real-world data analysis.

ESMO real world data and digital oncology·2026
Same author

Surrogates 20 years on: long-term psychological health, contact with surrogacy families, and thoughts and feelings about post-birth contact.

Human reproduction (Oxford, England)·2025
Same author

Self-monitoring of blood pressure following a stroke or transient ischaemic attack (TASMIN5S): a randomised controlled trial.

BMC cardiovascular disorders·2024
Same author

'It's all settled on the right page' surrogates' feelings and reflections of surrogacy two decades on.

Human reproduction (Oxford, England)·2024
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
See all related articles

Related Experiment Video

Updated: Sep 1, 2025

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

3.4K

Fast and Accurate Binary Response Mixed Model Analysis Via Expectation Propagation.

P Hall1, I M Johnstone2, J T Ormerod3

  • 1School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.

Journal of the American Statistical Association
|August 17, 2022
PubMed
Summary
This summary is machine-generated.

Expectation propagation offers a novel method for frequentist statistical inference, particularly for binary mixed models. This approach enables fast, accurate, and scalable analysis without requiring quadrature methods.

Keywords:
Best predictionGeneralized linear mixed modelsKullback–Leibler projectionMaximum likelihoodMessage passingQuasi-Newton methodsScalable statistical methodology

More Related Videos

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.2K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K

Related Experiment Videos

Last Updated: Sep 1, 2025

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

3.4K
Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.2K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K

Area of Science:

  • Statistics
  • Computational Statistics
  • Statistical Inference

Background:

  • Expectation propagation (EP) is a general method for approximating integrals in statistical inference.
  • Existing literature primarily focuses on EP within Bayesian inference frameworks.
  • The application of EP to frequentist inference remains less explored.

Purpose of the Study:

  • To investigate the utility of expectation propagation for frequentist statistical inference.
  • To develop a fast and accurate quadrature-free inference method for binary response mixed models.
  • To assess the performance of EP in approximating likelihood surfaces for complex models.

Main Methods:

  • Applied expectation propagation to likelihood-based inference for binary response mixed models.
  • Utilized a probit link function with multivariate random effects and higher nesting levels.
  • Employed asymptotic calculations to analyze the consistency of EP estimation.
  • Conducted numerical studies to evaluate the methodology's speed, accuracy, and scalability.

Main Results:

  • Demonstrated that expectation propagation can be effectively used for frequentist inference.
  • Achieved fast and accurate quadrature-free inference for binary mixed models with probit links.
  • Asymptotic calculations confirmed consistent estimation of the exact likelihood surface by EP.
  • Numerical results highlighted the methodology's speed, high accuracy, and scalability.

Conclusions:

  • Expectation propagation provides a viable and efficient alternative for frequentist inference in binary mixed models.
  • The developed method offers a scalable and accurate solution for analyzing complex binary data.
  • This work extends the application of expectation propagation beyond Bayesian contexts.