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)...
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: 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...
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
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,...
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower Kd...

You might also read

Related Articles

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

Sort by
Same author

Temporal changes in soil carbon and nitrogen in response to grazing management and vegetation cover in south-eastern Australia.

PloS one·2026
Same author

Fencing the Flux: Seasonal Trends, Environmental Drivers, and Mitigation Opportunities of Methane Emissions From Farm Dams.

Global change biology·2025
Same author

Removing dead trees will not save us from fast-moving wildfires.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Adjusted predictions for generalized estimating equations.

Biometrics·2025
Same author

Religious temples are long-term refuges for old trees in human-dominated landscapes in China.

Current biology : CB·2025
Same author

Maintaining robust terrestrial ecological monitoring amid technological advancements.

Trends in ecology & evolution·2025
Same journal

Thymidylate synthase inhibitory drugs induce p53-dependent pathways differently.

PloS one·2026
Same journal

Top-down and bottom-up attention for joint pattern classification and reconstruction.

PloS one·2026
Same journal

Short- and long-term scaling behavior of blood pressure and pulse arrival time during sleep in healthy controls and patients with obstructive sleep apnea.

PloS one·2026
Same journal

Double DQN-based secrecy energy efficiency and fairness performance in IRS-assisted NOMA systems with friendly jamming.

PloS one·2026
Same journal

10 recommendations for strengthening citizen science for improved societal and ecological outcomes: A co-produced analysis of challenges and opportunities in the 21st century.

PloS one·2026
Same journal

Paying in public: Peer effects, impression management, and willingness to pay on digital payment platforms.

PloS one·2026
See all related articles

Related Experiment Video

Updated: May 15, 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

Fitting and interpreting occupancy models.

Alan H Welsh1, David B Lindenmayer, Christine F Donnelly

  • 1Centre for Mathematics and its Applications, The Australian National University, Canberra, Australia. Alan.Welsh@anu.edu.au

Plos One
|January 18, 2013
PubMed
Summary
This summary is machine-generated.

Occupancy models can be difficult to fit, yielding unstable and biased estimates, especially with sparse data. Adjusting for non-detection may be as misleading as ignoring it, with ignoring non-detection sometimes being preferable.

More Related Videos

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data
08:12

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data

Published on: February 16, 2024

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

Related Experiment Videos

Last Updated: May 15, 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

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data
08:12

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data

Published on: February 16, 2024

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Published on: October 11, 2016

Area of Science:

  • Ecology
  • Statistical Modeling

Background:

  • Occupancy models are widely used in ecology to estimate species distribution.
  • Standard occupancy models assume perfect detection, which is often violated in practice.
  • Non-detection is a common issue in ecological surveys, leading to potential biases.

Purpose of the Study:

  • To investigate the challenges and biases associated with fitting occupancy models.
  • To compare the performance of adjusted occupancy models with models that ignore non-detection.

Main Methods:

  • Analysis of occupancy model estimation equations to identify multiple solutions and boundary estimates.
  • Evaluation of model stability and variability with sparse and dense data.
  • Assessment of bias in occupancy and detection estimates when abundance varies and depends on detection.

Main Results:

  • Occupancy models often have multiple solutions, including boundary estimates, leading to difficulties in accurate inference.
  • Estimates from occupancy models are unstable with sparse data and highly variable even in ideal conditions.
  • When abundance varies and affects detection, standard analyses suffer bias, asymmetric distributions, and slow convergence.

Conclusions:

  • Biases in adjusted occupancy models can be as severe as those from ignoring non-detection.
  • Detection error makes abundance unobservable, preventing bias assessment and adjustment.
  • Ignoring non-detection may sometimes yield more reliable results than attempting to adjust for it.