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Multivariable Analysis

Multivariable Analysis

Multivariable Analysis

A Practical Guide for Clinicians
2nd Edition
Mitchell H. Katz, University of California, San Francisco
May 2012
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Adobe eBook Reader
9781139239264
$62.00
USD
Adobe eBook Reader

    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

    See more reviews

    Product details

    May 2012
    Adobe eBook Reader
    9781139239264
    0 pages
    0kg
    26 b/w illus. 15 tables
    This ISBN is for an eBook version which is distributed on our behalf by a third party.

    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.
      Author
    • Mitchell H. Katz , University of California, San Francisco