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

Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K
Regression Analysis01:11

Regression Analysis

8.9K
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:
8.9K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
9.8K
Multiple Regression01:25

Multiple Regression

4.3K
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...
4.3K
Correlation and Regression00:53

Correlation and Regression

4.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
4.2K
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

69
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...
69

You might also read

Related Articles

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

Sort by
Same author

Completeness and accuracy of data transfer of routine maternal health services data in the greater Accra region.

BMC research notes·2015
Same author

Pragmatic randomized trials in drug development pose new ethical questions: a systematic review.

Drug discovery today·2015
Same author

Polysaccharide conjugate vaccine against pneumococcal pneumonia in adults.

The New England journal of medicine·2015
Same author

Common carotid intima-media thickness relates to cardiovascular events in adults aged <45 years.

Hypertension (Dallas, Tex. : 1979)·2015
Same author

A novel approach for establishing cardiovascular drug efficacy.

Nature reviews. Drug discovery·2014
Same author

Cardiovascular manifestations of HIV infection in children.

European journal of preventive cardiology·2014

Related Experiment Video

Updated: Apr 4, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.9K

Estimating interaction on an additive scale between continuous determinants in a logistic regression model.

Mirjam J Knol1, Ingeborg van der Tweel, Diederick E Grobbee

  • 1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands. m.j.knol@umcutrecht.nl

International Journal of Epidemiology
|August 30, 2007
PubMed
Summary
This summary is machine-generated.

This study introduces methods to quantify biologic interaction on an additive scale using logistic regression for continuous determinants. These methods help epidemiologists better understand risk factor interactions in health research.

More Related Videos

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Published on: July 30, 2020

3.4K
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

6.5K

Related Experiment Videos

Last Updated: Apr 4, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.9K
Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS
04:40

Tactile Semiautomatic Passive-Finger Angle Stimulator TSPAS

Published on: July 30, 2020

3.4K
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

6.5K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Research

Background:

  • Epidemiologic research often uses product terms in regression models to assess interaction.
  • Linear regression coefficients reflect additive interaction, while logistic regression coefficients reflect multiplicative interaction.
  • Estimating biologic interaction on an additive scale is crucial but challenging with continuous determinants.

Purpose of the Study:

  • To provide methods for estimating additive-scale interaction between continuous determinants using logistic regression.
  • To illustrate these methods with a practical clinical example.

Main Methods:

  • Derived formulas to quantify additive-scale interaction for continuous and dichotomous determinants using logistic regression.
  • Employed bootstrapping to calculate confidence intervals for interaction estimates.
  • Utilized data from the Utrecht Health Project for an empirical illustration.

Main Results:

  • Presented formulas for calculating additive interaction between one continuous and one dichotomous determinant.
  • Provided formulas for additive interaction between two continuous determinants.
  • Demonstrated application using age and body mass index predicting elevated diastolic blood pressure.

Conclusions:

  • The study offers practical methods and formulas for epidemiologists to calculate additive-scale interaction.
  • A spreadsheet with the proposed methods is freely available for use.
  • Enhances the understanding of risk factor interactions in epidemiologic studies.