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

Cluster Sampling Method01:20

Cluster Sampling Method

15.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.6K
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

2.7K
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
2.7K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

323
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...
323
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

311
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...
311
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

338
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...
338
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

738
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...
738

You might also read

Related Articles

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

Sort by
Same author

Surveillance of Pharmaceutical Risk-Mitigation Behavior: Applying and Comparing Statistical Process Control Methods Using Real World Data.

Learning health systems·2026
Same author

Scalable Bayesian Image-on-Scalar Regression for Population-Scale Neuroimaging Data Analysis.

Journal of the American Statistical Association·2026
Same author

Bayesian Image Mediation Analysis.

Journal of the American Statistical Association·2026
Same author

Relation of blood-based inflammation conditional networks to key immune health status and Alzheimer's biomarkers in aging adults.

Neurobiology of aging·2026
Same author

Design and implementation of an international multiarm, multistage master protocol for trials of complex traumatic wound care during active war in Ukraine.

The journal of trauma and acute care surgery·2026
Same author

Clinical epidemiology, microbiology, and care of war-related wounds in Ukraine.

The journal of trauma and acute care surgery·2026

Related Experiment Video

Updated: Mar 30, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K

Using Cox cluster processes to model latent pulse location patterns in hormone concentration data.

Nichole E Carlson1, Gary K Grunwald2, Timothy D Johnson3

  • 1Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA nichole.carlson@ucdenver.edu.

Biostatistics (Oxford, England)
|November 11, 2015
PubMed
Summary

This study introduces a new Bayesian model to analyze hormone pulse timing, revealing circadian rhythms and subject-specific clustering. The model improves understanding of stress hormone regulation in women.

Keywords:
Bayesian analysisDeconvolutionMixture modelsPoint processesPulsatile hormones

More Related Videos

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

16.5K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.3K

Related Experiment Videos

Last Updated: Mar 30, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K
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

16.5K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.3K

Area of Science:

  • Endocrinology and statistical modeling.
  • Computational biology and bioinformatics.
  • Neuroendocrinology and chronobiology.

Background:

  • Hormones, like stress hormones, are secreted in intermittent pulses, crucial for regulating bodily systems.
  • Characterizing hormone pulse timing is challenging due to latent pulse locations and time-varying secretion patterns.
  • Existing methods fail to account for subject-to-subject clustering in hormone pulse occurrences.

Purpose of the Study:

  • To develop a novel statistical model for analyzing the latent pulsatile secretion of hormones.
  • To capture circadian rhythms, inter-subject clustering, and external influences on hormone pulse timing.
  • To integrate this pulse location model with hormone concentration data for comprehensive analysis.

Main Methods:

  • Adaptation of a Bayesian Cox cluster process to model hormone pulse locations.
  • Integration of the pulse location model with a hormone concentration model.
  • Utilizing a spatial birth-and-death Markov chain Monte Carlo algorithm for parameter estimation.

Main Results:

  • The proposed Bayesian Cox cluster process effectively detects circadian rhythms in hormone pulse locations.
  • The model successfully identifies clustering of pulse locations across different subjects.
  • Exogenous controllers influencing hormone pulse events can be identified using this approach.

Conclusions:

  • The novel Bayesian Cox cluster process offers a robust framework for analyzing complex hormone secretion patterns.
  • This model enhances the understanding of the stress axis by revealing hidden dynamics in hormone pulsatility.
  • The approach is validated on simulated data and applied to human stress hormone data from women.