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

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)...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
Correlation of Experimental Data01:23

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...
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...
Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...

You might also read

Related Articles

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

Sort by
Same author

The circadian clock gene CYCLE as a potential target for disrupting blood-feeding behavior in the mosquito Culex pipiens.

PLoS neglected tropical diseases·2026
Same author

Corrigendum to "Pipiserpin orchestrates mosquito reproduction through dual control of vitellogenin integrity and 20-hydroxyecdysone-directed vitellogenesis" [Insect Biochem. Molec. Biol. 189 (2026) 104514].

Insect biochemistry and molecular biology·2026
Same author

Pipiserpin orchestrates mosquito reproduction through dual control of vitellogenin integrity and 20-hydroxyecdysone-directed vitellogenesis.

Insect biochemistry and molecular biology·2026
Same author

Thoracic epidural insertion using extended reality hologram assistance: a randomised trial of regional anaesthesia skill performance on the soft embalmed Thiel cadaver.

British journal of anaesthesia·2026
Same author

Lnc-Gm26626 in visceral adipose tissues participates in energy metabolism via IDH3α-associated tricarboxylic acid cycle activity.

The Journal of nutritional biochemistry·2025
Same author

Multi-oil fat emulsion improves postoperative nutritional status and reduces complications in patients with hilar cholangiocarcinoma.

Frontiers in nutrition·2025
Same journal

Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control.

Pharmaceutical statistics·2026
Same journal

Harmonic Fowlkes-Mallows Index for Medical Diagnostics Tests and Optimal Cut-Off Point Selection of Binary Diseases.

Pharmaceutical statistics·2026
Same journal

Early Phase Dose-Finding Designs for CAR-T Cell Therapies.

Pharmaceutical statistics·2026
Same journal

Optimizing Randomization Ratios in Clinical Trials With Survival Endpoints.

Pharmaceutical statistics·2026
Same journal

CUI-MET: A Clinical Utility Index Based Analysis and Decision Framework for Dose Optimization in Multiple-Dose, Multiple-Outcome Randomized Trials.

Pharmaceutical statistics·2026
Same journal

Will the Pharmaceutical Industry Need Statisticians in an AI World?

Pharmaceutical statistics·2026
See all related articles

Related Experiment Video

Updated: May 29, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Modeling data with structural and temporal correlation using lower level and higher level multilevel models.

Gareth James1, Yinghui Zhou, Sam Miller

  • 1Applied Statistics, University of Reading, Reading, UK.

Pharmaceutical Statistics
|September 30, 2011
PubMed
Summary
This summary is machine-generated.

Novel imaging techniques aid drug development. Higher-level multilevel models better explain variation in complex imaging data from clinical trials for atherosclerotic plaques.

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

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: May 29, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

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:

  • Medical Imaging
  • Biostatistics
  • Drug Development

Background:

  • Novel imaging techniques are crucial for understanding new drug mechanisms.
  • Large, complex datasets from imaging studies often pose statistical analysis challenges.
  • Magnetic resonance imaging (MRI) is used to assess atherosclerotic plaque changes.

Purpose of the Study:

  • To compare statistical models for analyzing complex imaging data in drug development.
  • To evaluate two-level and four-level multilevel models for MRI data in a clinical trial.
  • To determine the most effective statistical approach for handling correlations in imaging data.

Main Methods:

  • Utilized magnetic resonance imaging (MRI) data from a clinical trial.
  • Applied two-level and four-level multilevel statistical models.
  • Assessed changes in atherosclerotic plaques after treatment with a tool compound.

Main Results:

  • Both two-level and four-level models identified similar structural variables.
  • Higher-level (four-level) multilevel models explained a greater proportion of data variation.
  • The assumptions of higher-level multilevel models were better satisfied.

Conclusions:

  • Multilevel models are effective for analyzing complex imaging data in drug development.
  • Higher-level multilevel models offer advantages in explaining variance and model fit.
  • These findings support advanced statistical approaches for novel imaging data in clinical trials.