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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

299
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...
299
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

307
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...
307
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

1.3K
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...
1.3K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
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...
1.2K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

372
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...
372
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

41
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...
41

You might also read

Related Articles

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

Sort by
Same author

Targeted degradation of MDM2 overcomes feedback regulation of p53 signaling in Merkel cell carcinoma models.

The Journal of clinical investigation·2026
Same author

ESR1 mutations and CDK4/6 inhibitor choice shape clonal selection and adaptive cell states during acquired resistance.

Genome medicine·2026
Same author

A pan-cancer single-cell analysis of intratumoral copy number diversity and evolution.

Cancer discovery·2026
Same author

H3K27M-driven hypertranscription leads to a new targetable dependency in diffuse midline gliomas.

bioRxiv : the preprint server for biology·2026
Same author

Radiation Oncology-Biology Integration Network: Bridging the Gap between Biological Research and Clinical Practice.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

HER2 heterogeneous breast cancer models reveal novel therapeutic targets and subclonal dynamics during evolution to resistance to HER2-targeted therapies.

Cancer discovery·2026
Same journal

Modeling and analysis of forward and inverse kinematics for a flexible Stewart platform.

PloS one·2026
Same journal

Barriers and facilitators to healthcare utilization amongst people living with sickle cell disease in the United States: A scoping review.

PloS one·2026
Same journal

Enhancing data completeness in time series: Imputation strategies for missing data using significant periodically correlated components.

PloS one·2026
Same journal

Key targets and mechanisms by which gut microbiota-derived metabolites regulate Alzheimer's disease through the immune - inflammatory pathway: Based on network pharmacology and molecular docking.

PloS one·2026
Same journal

Grid-tied Transformer-less Boost Switched Capacitor Topology (TLBSCT) for PV applications.

PloS one·2026
Same journal

The load-velocity profiles and exercise-specific velocity zones for seven commonly used weightlifting exercises.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Feb 26, 2026

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

3.8K

Estimating mono- and bi-phasic regression parameters using a mixture piecewise linear Bayesian hierarchical model.

Rui Zhao1,2, Paul Catalano1,2, Victor G DeGruttola1

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, United States of America.

Plos One
|July 21, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian model to analyze patient data over time, revealing hidden patient subgroups and nonlinear trends. This approach improves understanding of treatment responses in complex diseases like multiple myeloma.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
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

11.7K

Related Experiment Videos

Last Updated: Feb 26, 2026

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

3.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
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

11.7K

Area of Science:

  • Biostatistics
  • Translational Medicine
  • Clinical Trial Analysis

Background:

  • Longitudinal data analysis is crucial for evaluating treatment effectiveness and predicting patient outcomes.
  • Existing methods often assume patient homogeneity and uniform response trajectories, limiting their applicability.
  • Tumor burden and biomarker dynamics over time are key indicators in clinical research.

Purpose of the Study:

  • To develop a novel statistical model addressing population heterogeneity and nonlinear biomarker-time relationships in longitudinal data.
  • To accurately classify patients based on their treatment response patterns.
  • To analyze complex longitudinal data from a multiple myeloma clinical trial.

Main Methods:

  • A mixture piecewise linear Bayesian hierarchical model was developed.
  • Simulations were used to validate the model's classification accuracy.
  • The model was applied to longitudinal data from a phase III clinical trial in multiple myeloma.

Main Results:

  • The proposed model achieved over 80% accuracy in classifying subjects across tested scenarios.
  • Analysis of multiple myeloma data revealed heterogeneous and nonlinear tumor burden trajectories.
  • Distinct differences in regression parameters and mixture distributions were observed between treatment cohorts.

Conclusions:

  • Longitudinal clinical trial data may contain unobserved patient subgroups and nonlinear relationships.
  • Accounting for heterogeneity and nonlinearity is essential for robust analysis of longitudinal data.
  • The developed Bayesian model offers a powerful tool for characterizing complex patient responses in clinical research.