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
6 - Setting Up a Multivariable Analysis
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
In setting up your model, include those variables, in addition to the risk factor or group assignment, that have been theorized or shown in prior research to be confounders or those that empirically are associated with the risk factor and the outcome in bivariate analysis.
Exclude variables that are on the intervening pathway between the risk factor and outcome, those that are extraneous because they are not on the causal pathway, redundant variables, and variables with a lot of missing data.
Sample size calculation for multivariable analysis is complicated but statistical programs exist to help you to calculate it. Missing data on independent variables can compromise your multivariable analysis. Several methods exist to compensate for missing independent data including deleting cases, using indicator variables to represent missing data and estimating the value of missing cases. Methods also exist for estimating missing outcome data using other data you have about the subject and multiple imputation.
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- Multivariable AnalysisA Practical Guide for Clinicians and Public Health Researchers, pp. 98 - 122Publisher: Cambridge University PressPrint publication year: 2025
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