Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Common uses of multivariable models
- 3 Outcome variables in multivariable analysis
- 4 Type of independent variables in multivariable analysis
- 5 Assumptions of multiple linear regression, multiple logistic regression, and proportional hazards analysis
- 6 Relationship of independent variables to one another
- 7 Setting up a multivariable analysis
- 8 Performing the analysis
- 9 Interpreting the analysis
- 10 Checking the assumptions of the analysis
- 11 Propensity scores
- 12 Correlated observations
- 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: 05 July 2011
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Common uses of multivariable models
- 3 Outcome variables in multivariable analysis
- 4 Type of independent variables in multivariable analysis
- 5 Assumptions of multiple linear regression, multiple logistic regression, and proportional hazards analysis
- 6 Relationship of independent variables to one another
- 7 Setting up a multivariable analysis
- 8 Performing the analysis
- 9 Interpreting the analysis
- 10 Checking the assumptions of the analysis
- 11 Propensity scores
- 12 Correlated observations
- 13 Validation of models
- 14 Special topics
- 15 Publishing your study
- 16 Summary: Steps for constructing a multivariable model
- Index
Summary
Step 1. Based on the type of outcome variable you have, use Table 3.1 to determine the type of multivariable model to perform (if you have repeated observations of your outcome see Table 12.2).
Step 2. Perform univariate statistics to understand the distribution of your independent and outcome variables. Assess for implausible values, significant departures from normal distribution of interval variables, gaps in values, and outliers (Section 5.7).
Step 3. Perform bivariate analysis of your independent variables against your outcome variable.
Step 4. If you have any nominal independent variables transform them into multiple dichotomous (“dummied”) variables (Section 4.2).
Step 5. Assess whether your data fit the assumptions of your multivariable model (linearity, normal distribution, equal variance) on a bivariate basis (Chapter 5). Transform or group any variables that show significant bivariate departures from the assumptions of your model (Sections 5.4, 5.5, 5.7, and 5.8).
Step 6. Run a correlation matrix. If any pair of independent variables are correlated at > 0.90 (multicollinearity), decide which one to keep and which one to exclude. If any pair of variables are correlated at 0.80 to 0.90 consider dropping one (Chapter 6).
Step 7. Assess how much missing data you will have in your multivariable analysis. Choose a strategy for dealing with missing cases from Table 7.4.
Step 8. Perform the analysis (Chapter 8).
Step 9. Review the multivariable correlation matrix to assess for multicollinearity. If you have evidence of serious multicollinearity, delete a variable (Chapter 6).
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- Information
- Multivariable AnalysisA Practical Guide for Clinicians, pp. 197 - 198Publisher: Cambridge University PressPrint publication year: 2006