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

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
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.
Introduction to Nonlinear Inequalities01:25

Introduction to Nonlinear Inequalities

Linear and nonlinear inequalities are fundamental for analyzing variable relationships and identifying ranges satisfying specific conditions. A linear inequality involves variables raised only to the first power, resulting in a straight-line graph. This line partitions the coordinate plane into two distinct regions: one that satisfies the inequality and one that does not. Each region represents a set of solutions where the linear relationship holds true under the specified constraint.Nonlinear...
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...
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...
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...

You might also read

Related Articles

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

Sort by
Same author

The Effects of Financial Structures to Increase Social Drivers of Health Investments in Medicaid: A Simulation Approach.

American journal of public health·2026
Same author

Surgical Approach and Long-Term Operative Recurrence Following Groin Hernia Repair.

JAMA surgery·2026
Same author

Receipt of Industry Payments and Surgeons' Adoption of Robotic-Assisted Surgery.

JAMA network open·2026
Same author

Introduction to entropy balancing: A case study on the association between statin therapy and outcomes after traumatic injury.

Surgery·2026
Same author

Timeliness of Antiresorptive Consolidation After Anabolic Therapy for Primary Fracture Prevention: A U.S. Cohort Study.

The Journal of clinical endocrinology and metabolism·2026
Same author

In Vitro Fertilization Utilization Rates and Outcomes in States With and Without Insurance Coverage Mandates for Male Infertility Care.

Urology·2026
Same journal

The Association Between Sepsis Coding and Payment to U.S. Hospitals.

Health services research·2026
Same journal

Stagnation in Achieving Recommended Methadone Doses in Opioid Use Disorder Treatment.

Health services research·2026
Same journal

Promoting Transplant Access Through Dialysis Facility Performance Metrics: A Double-Edged Sword.

Health services research·2026
Same journal

Understanding Medicaid Estate Recovery: The Experience of North Carolina and Policy Implications for Future Reforms.

Health services research·2026
Same journal

Racial Disparities and Personal Responsibility Incentives in Medicaid.

Health services research·2026
Same journal

Under-Documentation of Z-Codes in Hospitalizations of Homeless Shelter Users in New York City.

Health services research·2026
See all related articles

Related Experiment Video

Updated: May 27, 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

Interaction terms in nonlinear models.

Pinar Karaca-Mandic1, Edward C Norton, Bryan Dowd

  • 1Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA. pkmandic@umn.edu

Health Services Research
|November 19, 2011
PubMed
Summary
This summary is machine-generated.

Understanding interaction terms in nonlinear models is crucial for accurate interpretation. This study clarifies their use and calculation in various statistical models for better analysis.

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Related Experiment Videos

Last Updated: May 27, 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

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Area of Science:

  • Statistics
  • Econometrics
  • Social Sciences

Background:

  • Interaction terms are commonly used in multivariate analyses.
  • Interpreting interaction terms in linear models is straightforward but complex in nonlinear models.

Purpose of the Study:

  • To explain the motivation and interpretation of interaction terms in nonlinear models.
  • To provide guidance on calculating and interpreting interaction effects in various nonlinear statistical models.

Main Methods:

  • Discussion of the theoretical underpinnings of interaction terms in nonlinear models.
  • Graphical and equation-based explanations of interpretation differences between linear and nonlinear models.
  • Extension of methods to logit, probit, difference-in-differences, and panel data models.

Main Results:

  • Demonstration of interaction effect calculation and interpretation using a Stata dataset.
  • Highlighting new features in Stata 11 that facilitate interaction term analysis.
  • Provision of LIMDEP code for analysis.

Conclusions:

  • Clear understanding of interaction terms in nonlinear models is essential for correct substantive interpretation.
  • Accurate interpretation of interaction effects enhances the validity of statistical findings.