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
13 - Validation of Models
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
A valid model is one whether the inferences drawn from it are true. Many factors can threaten the validity of a model including imprecise or inaccurate measurements, bias in study design or in sampling, and misspecification of the model itself.
A key way to validate a model is to replicate the findings with new data. The best method of replication is collecting new data. However, when that is not possible, it is possible to perform a replicate by dividing the sample using a split-group, jackknife, or bootstrap method. Of these 3 methods, split-group is the strongest but requires a dataset large enough to split your sample. A bootstrap is the weakest method of replication, but produces more valid confidence intervals than a simple model.
Information
- Type
- Chapter
- Information
- Multivariable AnalysisA Practical Guide for Clinicians and Public Health Researchers, pp. 238 - 243Publisher: Cambridge University PressPrint publication year: 2025
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