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

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
Nonlinear Pharmacokinetics: Causes of Nonlinearity01:22

Nonlinear Pharmacokinetics: Causes of Nonlinearity

Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...
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...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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.

You might also read

Related Articles

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

Sort by
Same author

Australian guidelines for anal cancer screening using anal human papillomavirus testing with cytology triage in people living with HIV.

HIV medicine·2025
Same author

The attribution of human health outcomes to climate change: a transdisciplinary guidance document.

Climatic change·2025
Same author

The utility of behavioral biometrics in user authentication and demographic characteristic detection: a scoping review.

Systematic reviews·2024
Same author

Feasibility and preliminary efficacy of structured programming and a parent intervention to mitigate accelerated summer BMI gain: a pilot study.

Pilot and feasibility studies·2023
Same author

In praise of Prais-Winsten: An evaluation of methods used to account for autocorrelation in interrupted time series.

Statistics in medicine·2023
Same author

Correction to: Early-stage studies to larger-scale trials: investigators' perspectives on scaling-up childhood obesity interventions.

Pilot and feasibility studies·2022
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jun 9, 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

Distributed lag non-linear models.

A Gasparrini1, B Armstrong, M G Kenward

  • 1Public Health and Policy Department, London School of Hygiene and Tropical Medicine, Keppel Street, London W1C 7HT, U.K. antonio.gasparrini@lshtm.ac.uk

Statistics in Medicine
|September 3, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces distributed lag non-linear models (DLNM) to analyze environmental stressors with delayed effects. These models capture complex exposure-response relationships over time, improving environmental health research.

Related Experiment Videos

Last Updated: Jun 9, 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

Area of Science:

  • Environmental Epidemiology
  • Biostatistics
  • Time Series Analysis

Background:

  • Environmental stressors can exhibit delayed impacts, necessitating advanced statistical methods.
  • Traditional models often struggle to capture both non-linear exposure-response and temporal lag effects simultaneously.

Purpose of the Study:

  • To develop a flexible modeling framework, distributed lag non-linear models (DLNM), for analyzing delayed environmental exposure effects.
  • To unify existing approaches and introduce novel, more adaptable models for exposure-response-lag relationships.

Main Methods:

  • Introduced the concept of a 'cross-basis' to define a bi-dimensional function space.
  • Simultaneously modeled non-linear relationships and delayed effects of environmental predictors.
  • Implemented the DLNM framework in the R package 'dlnm'.

Main Results:

  • The DLNM framework provides a unified and flexible approach to model complex exposure-response-lag associations.
  • Demonstrated the application of DLNM using temperature and mortality data from New York (1987-2000).

Conclusions:

  • DLNM offers a powerful tool for investigating environmental exposures with delayed and non-linear impacts.
  • The methodology enhances the understanding of environmental health risks by accounting for temporal dimensions.