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

92
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...
92
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

136
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...
136
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

183
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
183
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

137
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...
137
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

416
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...
416
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

237
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...
237

You might also read

Related Articles

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

Sort by
Same author

MRI-guided detection of knee injuries as a concomitant lesion of femoral shaft fractures: a systematic review and meta-analysis.

EFORT open reviews·2026
Same author

Clinical and imaging features of haemochromatosis arthropathy: a systematic literature review and meta-analysis to inform the development of EULAR classification criteria.

EULAR rheumatology open·2026
Same author

Psychotherapy, Antidepressants, and Combined Treatment for Depression: A Network Meta-analysis on Social Functioning Outcomes.

Psychotherapy and psychosomatics·2026
Same author

Impact of support levels on effectiveness and drop-out of internet-based interventions for depression: network meta-analysis.

The British journal of psychiatry : the journal of mental science·2026
Same author

Long-term effects of psychotherapies for depression: an advanced meta-analysis.

World psychiatry : official journal of the World Psychiatric Association (WPA)·2026
Same author

Analysing complex interventions using component network meta-analysis.

BMJ (Clinical research ed.)·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Oct 1, 2025

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.3K

Bayesian models for aggregate and individual patient data component network meta-analysis.

Orestis Efthimiou1,2,3, Michael Seo1,4, Eirini Karyotaki5,6,7

  • 1Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.

Statistics in Medicine
|March 9, 2022
PubMed
Summary
This summary is machine-generated.

Component network meta-analysis (CNMA) models can identify interactions between complex intervention components. These Bayesian models integrate aggregate and individual patient data for personalized treatment effect estimates.

Keywords:
complex interventionscompositemodel selectionmultiple treatments

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

Related Experiment Videos

Last Updated: Oct 1, 2025

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.0K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.8K

Area of Science:

  • Biostatistics
  • Medical Informatics
  • Clinical Epidemiology

Background:

  • Network meta-analysis synthesizes evidence for multiple treatments.
  • Complex interventions comprise multiple components, making analysis challenging.
  • Component network meta-analysis (CNMA) aims to disentangle component effects.

Purpose of the Study:

  • To present Bayesian CNMA models for identifying interactions between intervention components.
  • To extend CNMA models to incorporate individual patient data (IPD).
  • To facilitate personalized relative treatment effect estimation.

Main Methods:

  • Developed two Bayesian CNMA models using variable selection (stochastic search variable selection, Bayesian LASSO).
  • Incorporated prior information for significant interactions.
  • Extended models to combine aggregate data with individual patient data (IPD).

Main Results:

  • Demonstrated model utility with real datasets from panic disorder, depression, and multiple myeloma studies.
  • Successfully identified prominent interactions between intervention components.
  • Showcased the potential for personalized treatment effect predictions.

Conclusions:

  • Bayesian CNMA models effectively identify component interactions in complex interventions.
  • Integrating IPD enhances the precision of treatment effect estimates.
  • Developed methods enable personalized medicine through tailored treatment effect predictions.