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

Pharmacodynamic Models: Logarithmic Concentration–Effect Model01:15

Pharmacodynamic Models: Logarithmic Concentration–Effect Model

The log-linear model is a pharmacological framework used to describe the relationship between drug concentration and its effect. This model is particularly relevant when the observed effects range between 20% and 80% of the drug’s maximum effect (Emax), where a near-linear relationship is observed between the log of drug concentration and the measured effect. However, the log-linear model does not predict the maximum possible effect (Emax) or the effect at zero drug concentration, limiting its...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Pharmacodynamic Models: Linear Concentration–Effect Model01:15

Pharmacodynamic Models: Linear Concentration–Effect Model

The linear concentration–effect model, underpinned by the principle that pharmacological effect (E) is directly proportional to plasma drug concentration (C), emerges as a pivotal simplification of the Emax model for conditions where C is significantly less than EC50. This model portrays a linear trajectory of the concentration–effect relationship when drug levels are markedly below the EC50 threshold.Despite its inherent assumption of continuous effect augmentation with increasing drug...
Regression Analysis01:11

Regression Analysis

Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
Biostatistics: Overview01:20

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...

You might also read

Related Articles

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

Sort by
Same author

Expert consensus on the detection and clinical application of tumor mutational burden.

Cancer biology & medicine·2026
Same author

Scalable ultrathin separator coatings engineered by initiated chemical vapor deposition for high-performance Lithium metal batteries.

Journal of colloid and interface science·2026
Same author

Successful Management of Coronary Guidewire Fracture Using Intravascular Ultrasound-Guided Stent Jailing Technique During Retrograde Chronic Total Occlusion Recanalization.

Clinical case reports·2026
Same author

Association Between Heroin Use and Depression: NHANES 2005-2018.

Addiction biology·2026
Same author

Persistence of interleukin-17 and interleukin-23 inhibitors in patients with plaque psoriasis: a real-world study in Taiwan.

The Journal of dermatological treatment·2026
Same author

Two Decades of Real-World Study in Newly Diagnosed Multiple Myeloma: Evolving Treatment and Outcomes in China with Reference to the United States.

Cancers·2026
Same journal

[Research progress on the impact of the digital information environment on the health of children and adolescents].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2026
Same journal

[Exploration and practice of ideological and political education integration in the "One Core, Two Integrations" curriculum model for epidemiology].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2026
Same journal

[Progress in research of visualization of ideology and politics elements in curriculum and its importance for <i>Epidemiology</i> curriculum].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2026
Same journal

[Operation of WeChat official accounts of <i>Chinese Journal of Epidemiology</i>].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2026
Same journal

[Study on the risk factors of development for mild cognitive impairment to Alzheimer's disease based on the competitive risk joint model].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2026
Same journal

[Mendelian randomization study on related factors for esophageal adenocarcinoma and Barrett's esophagus].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2026
See all related articles

Related Experiment Video

Updated: Jun 5, 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 between continuous variables in logistic regression model].

Hong Qiu1, Ignatius Tak-Sun Yu, Lap Ah Tse

  • 1School of Public Health and Primary Care, Chinese University of Hong Kong, H.K.S.A.R.

Zhonghua Liu Xing Bing Xue Za Zhi = Zhonghua Liuxingbingxue Zazhi
|December 18, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method to assess biological interactions on an additive scale, expanding beyond previous research limited to dichotomous factors. The bootstrap re-sampling method with R software provides confidence intervals for interactions involving continuous variables.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Experiment Videos

Last Updated: Jun 5, 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

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Epidemiology
  • Biostatistics

Context:

  • Traditional logistic regression models interaction as departure from multiplicativity.
  • Previous research on additive scale interaction estimation was limited to two dichotomous factors.

Purpose:

  • To present a novel method for examining biological interaction as departure from additivity.
  • To extend interaction analysis to include continuous variables and mixed variable types (continuous and categorical).

Summary:

  • The study proposes and illustrates a method to estimate interaction on the additive scale for continuous variables or one continuous and one categorical variable.
  • Utilizes logistic regression and the bootstrap re-sampling technique for confidence interval calculation.
  • Applies the method to a lung cancer case-control study in Hong Kong.

Impact:

  • Enables more accurate biological interaction assessment in epidemiological and clinical research.
  • Provides a flexible statistical approach for complex variable relationships.
  • Facilitates the use of free software R for advanced interaction analysis.