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
2 - Common Uses of Multivariable Models
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 used for four major types of studies: observational studies of etiology, randomized and nonrandomized intervention studies, studies of diagnosis, and studies of prognosis.
For observational studies, whether etiologic or intervention, the most important reason to do multivariable analysis is to eliminate confounding, since in observational studies the groups are not randomly assigned. With randomized studies, multiple analysis is used to adjust for baseline differences that occurred by chance, to identify other independent predictors of outcome besides the randomized group, and x.
With studies of diagnosis, multivariable analysis is used to identify the best combination of diagnostic information to determine whether a person has a particular disease. Multivariable analysis can also be used to predict the prognosis of a group of patients with a particular set of known prognostic factors.
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- Information
- Multivariable AnalysisA Practical Guide for Clinicians and Public Health Researchers, pp. 14 - 24Publisher: Cambridge University PressPrint publication year: 2025
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