Multivariable Analysis
Multivariable Analysis
This new edition has been fully revised to build on the enormous success of its popular predecessor. It now includes new features introduced by readers' requests including a new chapter on propensity score, more detail on clustered data and Poisson regression and a new section on analysis of variance. As before it describes how to perform and interpret multivariable analysis, using plain language rather than complex derivations and mathematical formulae. It is the perfect introduction for all clinical researchers. It focuses on the nuts and bolts of performing research and prepares the reader to perform and interpret multivariable models. Numerous tables, graphs and tips help to simplify and explain the process of performing multivariable analysis. The text is illustrated with many up-to-date examples from the medical literature on how to use multivariable analysis in clinical practice and in research.
- Provides a nonmathematical introduction
- Nuts and bolts practical approach for clinical relevance
- Provides answers to basic questions
Reviews & endorsements
'This book had an enthusiastic first outing, and certainly this second edition is worth the price for a good reference.' Kentucky Medical Journal
Product details
No date availablePaperback
9780521549851
220 pages
245 × 190 × 11 mm
0.491kg
26 b/w illus. 15 tables
Table of Contents
- Preface
- 1. Introduction
- 2. Common uses of multivariable models
- 3. Outcome variables in multivariable analysis
- 4.Types of independent variables in multivariable analysis
- 5. Assumptions of multiple linear regression, 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.
