Book contents
- Multivariable Analysis
- Multivariable Analysis
- Copyright page
- Contents
- Preface
- 1 Introduction
- 2 Common Uses of Multivariable Models
- 3 Outcome Variables in Multivariable Analysis
- 4 Independent Variables in Multivariable Analysis
- 5 Relationship of Independent Variables to One Another
- 6 Setting Up a Multivariable Analysis
- 7 Performing the Analysis
- 8 Interpreting the Results
- 9 Delving Deeper: Checking the Underlying Assumptions of the Analysis
- 10 Propensity Scores
- 11 Correlated Observations
- 12 Sensitivity Analysis
- 13 Validation of Models
- 14 Special Topics
- 15 Publishing Your Study
- 16 Summary: Steps for Constructing a Multivariable Model
- Index
5 - Relationship of Independent Variables to One Another
Published online by Cambridge University Press: 09 October 2025
Book contents
- Multivariable Analysis
- Multivariable Analysis
- Copyright page
- Contents
- Preface
- 1 Introduction
- 2 Common Uses of Multivariable Models
- 3 Outcome Variables in Multivariable Analysis
- 4 Independent Variables in Multivariable Analysis
- 5 Relationship of Independent Variables to One Another
- 6 Setting Up a Multivariable Analysis
- 7 Performing the Analysis
- 8 Interpreting the Results
- 9 Delving Deeper: Checking the Underlying Assumptions of the Analysis
- 10 Propensity Scores
- 11 Correlated Observations
- 12 Sensitivity Analysis
- 13 Validation of Models
- 14 Special Topics
- 15 Publishing Your Study
- 16 Summary: Steps for Constructing a Multivariable Model
- Index
Summary
The strength of multivariable analysis is its ability to determine how multiple independent variables, which are related to one another, are related to an outcome. However, if two variables are so closely correlated that if you know the value for one variable you know the value of the other, multivariable analysis cannot separately assess the impact of these two variables on outcome. This problem is called multicollinearity.
To assess whether there is multicollinearity, investigators should first run a correlation matrix. However, the matrix only tells you the relationship between any two independent variables. Harder to detect is whether a combination of variables accounts for another variable’s value. Two related measure of muticollinearity are tolerance and the reciprocal of tolerance: the variation inflation factor. If you have variables that are highly related, you can omit one or more of the variables, use an “and/or” clause or create a scale.
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- Information
- Multivariable AnalysisA Practical Guide for Clinicians and Public Health Researchers, pp. 93 - 97Publisher: Cambridge University PressPrint publication year: 2025
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