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Related Experiment Video

Updated: May 31, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

Interaction on an additive scale.

Renée de Mutsert¹, Dinanda J de Jager, Kitty J Jager

¹Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands.

Nephron. Clinical Practice

|July 16, 2011

Summary

This study explains statistical interaction, which examines how risk factors affect disease or mortality differently across patient groups. Understanding interaction is crucial for public health and requires reporting separate and joint effects.

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Area of Science:

Epidemiology
Biostatistics
Public Health

Background:

Clinical epidemiological studies often assess associations between risk factors and disease incidence or mortality.
Investigating differential effects or joint effects across patient strata is of significant interest.

Purpose of the Study:

To examine the concept and assessment of statistical interaction between two risk factors in epidemiological studies.
To clarify the relevance of interaction on additive versus multiplicative scales from a public health perspective.

Main Methods:

Statistical interaction is examined by including a product term of two risk factors in a statistical model.
The evaluation of statistical interaction assesses deviations from additivity or multiplicativity.

Main Results:

Interaction analysis helps determine if the effect of one risk factor varies across strata defined by another risk factor.
Assessment of interaction on an additive scale is often most relevant for public health implications.

Conclusions:

Transparent reporting of interaction effects should include separate and joint effects relative to a joint reference group.
This approach allows for the evaluation of interaction on both additive and multiplicative scales, enhancing public health relevance.