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
16 - Summary: Steps for Constructing a Multivariable Model
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
Choose the type of multivariable model based on the type of outcome variable you have. Perform univariate statistics to understand the distribution of your independent and outcome variables. Perform bivariate analysis of your independent variables. Run a correlation matrix to understand how your independent variables are related to another. Assess your missing data. Perform your analysis and assess how well your model fits the data. Assess the strength of your individual covariates in estimating outcome. Use regression diagnostics to assess the underlying assumptions of your model. Perform sensitivity analyses to assess the robustness of your findings and consider whether it would be possible to validate your model. Publish your work and soak up the glory.
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- Information
- Multivariable AnalysisA Practical Guide for Clinicians and Public Health Researchers, pp. 259 - 260Publisher: Cambridge University PressPrint publication year: 2025
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