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

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

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...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

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 assumptions,...
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...

You might also read

Related Articles

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

Sort by
Same author

MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosis.

IEEE transactions on medical imaging·2026
Same author

Deep learning enabled decision support systems in epilepsy surgery: a scoping review.

npj health systems·2026
Same author

Two-Stage Decoupling Framework for Variable-Length Glaucoma Prognosis.

Learning with longitudinal medical images and data : first International Workshop, LMID 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 27, 2025, Proceedings. International Workshop on Learning with Longitudi...·2026
Same author

Comparative Analysis of Physiological and Biochemical Responses Between Compatible and Incompatible Graft Combinations of <i>Cyclocarya paliurus</i>.

Plants (Basel, Switzerland)·2026
Same author

Latent profile analysis of depressive symptoms and its associations with problematic social media use and demographic characteristics among Chinese college students.

BMC psychology·2026
Same author

Development of a Novel Mouse Model of Severe Acute Pancreatitis with Pathological Features Recapitulating Human Disease.

Digestive diseases and sciences·2026

Related Experiment Video

Updated: Jul 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Variance-Consistent Covariate Modeling from Posterior Summaries in Population Pharmacokinetics.

Junya Ooka1, Mizuki Uno1, Yuta Nakamaru1

  • 1Department of Quantitative Pharmaceutics, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshidashimoadachi-Cho, Sakyo-Ku, Kyoto, 606-8501, Japan.

Pharmaceutical Research
|July 9, 2026
PubMed
Summary

A new variance-consistent framework offers computationally scalable covariate modeling in nonlinear mixed-effects analysis. This method mitigates shrinkage bias and accurately identifies covariate effects without repeated model refitting.

Keywords:
covariate modelingempirical bayes estimatespopulation pharmacokineticsposterior summariesshrinkage bias

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Related Experiment Videos

Last Updated: Jul 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Pharmacokinetics and Pharmacodynamics (PKPD)
  • Statistical Modeling
  • Population Analysis

Background:

  • Systematic covariate modeling in nonlinear mixed-effects (NLME) analysis is computationally demanding due to repeated data refitting.
  • Empirical Bayes estimates (EBEs) can lead to shrinkage bias, attenuating variability and distorting covariance structures.

Purpose of the Study:

  • To introduce a variance-consistent framework for covariate modeling that avoids repeated refitting.
  • To enable efficient and accurate covariate identification in population PKPD analysis.

Main Methods:

  • The approach uses subject-specific posterior means and covariances from a single NLME base model fit.
  • A variance-matching penalty ensures consistency between total between-subject covariance and base model estimates.
  • Performance was compared against EBE regression, two-stage Bayesian estimation, and standard NLME covariate modeling.

Main Results:

  • The proposed method yielded unbiased covariate-effect parameter estimates, unlike EBE regression and two-stage Bayesian methods, which showed attenuation under shrinkage.
  • It successfully recovered covariate-effect estimates comparable to those from NLME analysis.
  • The framework demonstrated high computational scalability, reproducing NLME-based stepwise covariate selection without repeated data refitting.

Conclusions:

  • Variance-consistent posterior-based covariate modeling offers a statistically sound and computationally efficient solution for covariate identification in population PKPD studies.
  • This framework enhances systematic covariate analysis by preserving covariance structures and mitigating bias.