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
3 - Outcome Variables in 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
The choice of multivariable model depends primarily on the type of outcome variable. Use multiple linear regression and analysis of variance for interval outcomes, multiple logistic regression and log-binomial regression with dichotomous outcomes, proportion odds regression with ordinal outcomes, multinomial logistic regression for nominal outcomes, proportional hazards analysis for time to outcome, Poisson regression and negative binomial regression for counts and for incidence rates. Each model has a different set of underlying assumptions. All of the models assume that there is only one observation of outcome for each subject.
Keywords
Intervaldichotomousordinalnominaltime to outcomecountsincidence ratesmultiple linear regressionanalysis of variancemultiple logistic regressionproportional odds regressionmultinomial logistic regressionproportional hazards analysispoisson regressionnegative binomial regressionKaplan-Meier curveslog rankcensoringCox regressionper-protocolintention-to-treat
Information
- Type
- Chapter
- Information
- Multivariable AnalysisA Practical Guide for Clinicians and Public Health Researchers, pp. 25 - 76Publisher: Cambridge University PressPrint publication year: 2025
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