Now in its third edition, this highly successful text has been fully revised and updated with expanded sections on cutting-edge techniques including Poisson regression, negative binomial regression, multinomial logistic regression and proportional odds regression. As before, it focuses on easy-to-follow explanations of complicated multivariable techniques. It is the perfect introduction for all clinical researchers. It describes how to perform and interpret multivariable analysis, using plain language rather than complex derivations and mathematical formulae. It focuses on the nuts and bolts of performing research, and prepares the reader to set up, perform and interpret multivariable models. Numerous tables, graphs and tips help to demystify 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.
Features
• Marginal notes are included with research tips and definitions to help reinforce the key messages • Presents a practical, non-mathematical approach so the book is accessible to a wide audience • Illustrated throughout with up-to-date examples from the medical literature, tables and graphs to help simplify the process of performing multivariable analysis
Reviews of the second edition:
'Katz provides a comprehensive review of multivariable analysis to illuminate an often confusing topic for clinicians, particularly clinician scientists. The chapter on the assumptions of multivariable analysis provides excellent examples and tips throughout.' Myra A. Kleinpeter, Journal of the National Medical Association 'This book had an enthusiastic first outing, and certainly this second edition is worth the price for a good reference.' Kentucky Medical Journal
Table of 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.
