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Updated: Jun 22, 2026

Diagonal Method to Measure Synergy Among Any Number of Drugs
Published on: June 21, 2018
The synergy factor: a statistic to measure interactions in complex diseases
Mario Cortina-Borja1, A David Smith, Onofre Combarros
1Centre for Paediatric Epidemiology and Biostatistics, Institute of Child Health, University College London, 30 Guilford Street, London,WC1N 1EH, UK. m.cortina@ich.ucl.ac.uk
A new Synergy Factor (SF) method simplifies assessing interactions between genetic and environmental factors in complex diseases. This tool aids researchers, even non-statisticians, in analyzing published data for disease susceptibility interactions.
Area of Science:
- Epidemiology
- Biostatistics
- Genetics
Background:
- Understanding complex diseases requires analyzing interactions between genetic polymorphisms and environmental exposures.
- A lack of accessible methods for quantifying these interactions has led to confusion in the field.
- There is a need for a user-friendly tool to measure the size and significance of interactions using summarized data.
Purpose of the Study:
- To introduce and describe the properties of the Synergy Factor (SF) for assessing binary interactions in case-control studies.
- To present novel characteristics of SF, including power calculation for detecting synergistic effects and its application in meta-analyses.
- To provide an accessible method for non-statisticians to analyze interaction effects using summarized data.
Main Methods:
- The study describes the Synergy Factor (SF) as a method for assessing binary interactions.
- SF calculations can be performed using provided Excel programs.
- The method is applicable to any susceptibility factor with dichotomized data, including primary or summarized datasets.
Main Results:
- The Synergy Factor (SF) provides an assessment of binary interactions in case-control studies.
- An example in Alzheimer's disease demonstrated a significant interaction between a BACE1 polymorphism and APOE4 (SF = 2.5, 95% CI: 1.5-4.2, p = 0.0001).
- SF allows for power estimation and application in meta-analyses.
Conclusions:
- Synergy factors are easy to use and interpret, offering a clear method for interaction analysis.
- The SF method is versatile, applicable to datasets of any size, including published data.
- Novel features like power estimation and meta-analysis capabilities enhance its utility for researchers.
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