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
12 - Sensitivity 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
Sensitivity analysis tests how robust the results are to changes in the underlying assumptions of your analysis. In other words, if you made plausible changes in your assumptions, would you still draw the same conclusions? The changes could be a more restrictive or inclusive sample, a different way to measure your variables, a different way for handling missing data, or a change of a different feature of your analysis. With sensitivity analysis you cannot lose. If you vary the assumptions of your analysis and you get the same result, you will have more confidence in the conclusions of your study. Conversely, if plausible changes in your assumptions lead to a different conclusion, you will have learned something important. A common assumption tested in sensitivity analysis is that there are no unmeasured confounders, which can be tested with E values or falsification analysis. Other common assumptions tested are that losses to follow-up are random, that the sample is unbiased, that there is the correct exposure period and follow-up period, that there is a biased predictor or outcome, or that the model is misspecified.
Keywords
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- Chapter
- Information
- Multivariable AnalysisA Practical Guide for Clinicians and Public Health Researchers, pp. 225 - 237Publisher: Cambridge University PressPrint publication year: 2025
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