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

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

1.4K
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...
1.4K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.7K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.7K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

393
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...
393
Odds Ratio01:09

Odds Ratio

2.2K
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
2.2K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

709
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
709
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

424
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
424

You might also read

Related Articles

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

Sort by
Same author

PROFET Predicts Continuous Gene Expression Dynamics from scRNA-seq Data to Elucidate Heterogeneity of Cancer Treatment Responses.

bioRxiv : the preprint server for biology·2025
Same author

The stress-free state of human erythrocytes: Data-driven inference of a transferable RBC model.

Biophysical journal·2023
Same author

Cumulant GAN.

IEEE transactions on neural networks and learning systems·2022
Same author

AI support for ethical decision-making around resuscitation: proceed with care.

Journal of medical ethics·2021
Same author

Data-driven prediction and origin identification of epidemics in population networks.

Royal Society open science·2021
Same author

Optimal allocation of limited test resources for the quantification of COVID-19 infections.

Swiss medical weekly·2020

Related Experiment Video

Updated: Mar 24, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Efficient estimators for likelihood ratio sensitivity indices of complex stochastic dynamics.

Georgios Arampatzis1, Markos A Katsoulakis1, Luc Rey-Bellet1

  • 1Department of Mathematics and Statistics, University of Massachusetts, Amherst, Massachusetts 01003, USA.

The Journal of Chemical Physics
|March 17, 2016
PubMed
Summary

Centered likelihood ratio estimators offer efficient sensitivity analysis for complex stochastic dynamics. These methods provide low variance for long-time simulations and parameter screening, applicable across various scientific models.

More Related Videos

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.4K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K

Related Experiment Videos

Last Updated: Mar 24, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K
Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

7.4K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K

Area of Science:

  • Computational science and applied mathematics
  • Statistical inference for complex systems

Background:

  • Sensitivity analysis is crucial for understanding complex stochastic dynamics.
  • Existing methods often struggle with long-time simulations and high-dimensional parameter spaces.

Purpose of the Study:

  • To introduce efficient centered likelihood ratio estimators for sensitivity indices.
  • To enable robust sensitivity analysis in long-time and steady-state regimes.
  • To facilitate fast screening of insensitive parameters in complex models.

Main Methods:

  • Development of centered likelihood ratio estimators.
  • Novel covariance formulation of the likelihood ratio.
  • Integration of Fisher information matrix for stochastic dynamics.

Main Results:

  • Estimators exhibit high efficiency with low, time-constant variance.
  • Demonstrated suitability for long-time and steady-state sensitivity analysis.
  • New covariance formulation aids in rapid identification of insensitive parameters.

Conclusions:

  • The proposed estimators are highly efficient and suitable for various stochastic dynamics.
  • Applicable to chemical reaction networks, Langevin equations, and financial models.
  • Simple implementation within existing simulation algorithms without modification.