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 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 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...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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

Double immunohistochemistry to detect transglutaminase 2-IgA deposits in celiac children: a multicentre study.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver·2026
Same author

From diverticulosis to complicated diverticular disease: Progression of myogenic alterations and oxidative imbalance.

Neurogastroenterology and motility·2024
Same author

m6A modification inhibits miRNAs' intracellular function, favoring their extracellular export for intercellular communication.

Cell reports·2024
Same author

Bayesian inference for discretely observed continuous time multi-state models.

Statistics in medicine·2022
Same author

Bayesian analysis of one-inflated models for elusive population size estimation.

Biometrical journal. Biometrische Zeitschrift·2022
Same author

A new double immunohistochemistry method to detect mucosal anti-transglutaminase IgA deposits in coeliac children.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver·2021

Related Experiment Video

Updated: Jul 1, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A Bayesian model averaging approach for cost-effectiveness analyses.

Caterina Conigliani1, Andrea Tancredi

  • 1Dipartimento di Economia, Università Roma Tre, Roma, Italy. caterina.conigliani@eco.uniroma3.it

Health Economics
|September 16, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces Bayesian model averaging to accurately assess technology cost-effectiveness, especially with skewed cost data. This method improves upon traditional approaches by incorporating model uncertainty for better decision-making.

More Related Videos

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

Related Experiment Videos

Last Updated: Jul 1, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

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

Area of Science:

  • Health Economics
  • Biostatistics
  • Clinical Trial Analysis

Background:

  • Assessing technology cost-effectiveness relies on clinical trial data for costs and effects.
  • Skewed and heavy-tailed cost distributions pose challenges for traditional probabilistic modeling.
  • Accurate modeling of cost distribution tails is crucial for estimating population means.

Purpose of the Study:

  • To develop a robust method for cost-effectiveness analysis (CEA) with complex cost data.
  • To integrate model uncertainty into the analysis of cost data within CEA.
  • To compare a novel Bayesian approach with existing semi-parametric methods.

Main Methods:

  • Bayesian model averaging (BMA) was employed to handle uncertainty in cost data distributions.
  • A set of plausible parametric models for costs was specified.
  • Mean costs were estimated using a weighted average of posterior expectations, with weights from posterior model probabilities.

Main Results:

  • The BMA approach effectively integrates uncertainty about cost data distributions.
  • Results were compared against a semi-parametric method that makes fewer distributional assumptions.
  • The study demonstrates a more realistic approach to modeling skewed cost data in CEA.

Conclusions:

  • Bayesian model averaging offers a superior method for cost-effectiveness analysis when dealing with challenging cost data distributions.
  • This approach enhances the reliability of cost-effectiveness assessments by accounting for model uncertainty.
  • The findings support the use of BMA for more accurate health economic evaluations.