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...
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

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...
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)...
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: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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

Association between type 2 diabetes mellitus and anthropometric measurements - a case control study in South India.

Journal of preventive medicine and hygiene·2017
Same author

Primary Parotid Lymphoma From A Regional Cancer Center in South India.

The Gulf journal of oncology·2016
Same author

Cell-free epstein-barr viral loads in childhood hodgkin lymphoma: a study from South India.

Pediatric hematology and oncology·2013
Same author

Studies on formation of unconfined detonable vapor cloud using explosive means.

Journal of hazardous materials·2013
Same author

Leukemic relapse masquerading as a breast lump: an unusual manifestation.

Indian journal of cancer·2011
Same author

On the significance of the additional information obtained by the inclusion of some extra variables in the discrimination of populations.

Current science·2010

Related Experiment Video

Updated: Jul 11, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Simultaneous estimation of parameters in different linear models and applications to biometric problems.

C R Rao

    Biometrics
    |June 1, 1975
    PubMed
    Summary

    This study introduces an Empirical Bayes procedure for estimating vector parameters in Gauss-Markoff linear models, showing it outperforms least squares estimators. It also explores balancing decision-maker and individual losses and predicting future observations.

    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

    P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
    06:09

    P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

    Published on: September 8, 2023

    Related Experiment Videos

    Last Updated: Jul 11, 2026

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    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

    P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
    06:09

    P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

    Published on: September 8, 2023

    Area of Science:

    • Statistics
    • Econometrics
    • Linear Models

    Background:

    • Simultaneous estimation of vector parameters is crucial in statistical modeling.
    • Gauss-Markoff linear models are widely used in various scientific fields.
    • Traditional least squares estimators have limitations in certain estimation scenarios.

    Purpose of the Study:

    • To apply the Empirical Bayes procedure for simultaneous estimation of vector parameters in multiple Gauss-Markoff linear models.
    • To compare the performance of Empirical Bayes estimators against least squares estimators using a quadratic loss function.
    • To propose and investigate methods for distinguishing and balancing decision-maker and individual losses in parameter estimation.
    • To address the problem of predicting future observations within a linear model framework.

    Main Methods:

    • Utilizing the Empirical Bayes procedure for simultaneous estimation.
    • Employing a quadratic loss function for performance evaluation.
    • Developing a novel approach to differentiate and balance decision-maker and individual losses.
    • Considering prediction of future observations in linear models.

    Main Results:

    • Empirical Bayes estimators demonstrate superior performance compared to least squares estimators under a quadratic loss function.
    • A method for distinguishing between decision-maker and individual losses has been proposed.
    • The study addresses the prediction of future observations in linear models.

    Conclusions:

    • The Empirical Bayes procedure offers an improved approach to simultaneous parameter estimation in Gauss-Markoff linear models.
    • Further research is needed to fully explore the proposed method for balancing decision-maker and individual losses.
    • The findings contribute to enhanced statistical inference and prediction in linear modeling contexts.