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
1 - Introduction
Published online by Cambridge University Press: aN Invalid Date NaN
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
Multivariable analysis is needed because most events, whether medical, politica, social, or personal, have multiple causes. And these causes are related to one another. Multivariable analysis enables us to determine the relative contributions of different causes to a single event or outcome.
Multivariable analysis enables us to identify and adjust for confounders. Confounders are associated with the risk factor and causally related to the outcome. Adjustment for confounders is key to distinguishing important etiologic risk factors from variables that only appear to be associated with outcomes due to their association with the true risk factor.
Stratification can also be used for identifying independent relationships between risk factors and outcomes but becomes too cumbersome when there are more than one or two possible confounders.
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- Multivariable AnalysisA Practical Guide for Clinicians and Public Health Researchers, pp. 1 - 13Publisher: Cambridge University PressPrint publication year: 2025
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