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Multivariable Analysis
A Practical Guide for Clinicians and Public Health Researchers

3rd Edition

$66.00 (M)

  • Date Published: April 2011
  • availability: In stock
  • format: Paperback
  • isbn: 9780521141079

$66.00 (M)
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About the Authors
  • 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.

    • 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
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    Product details

    • Edition: 3rd Edition
    • Date Published: April 2011
    • format: Paperback
    • isbn: 9780521141079
    • length: 245 pages
    • dimensions: 246 x 190 x 11 mm
    • weight: 0.56kg
    • contains: 30 b/w illus. 40 tables
    • availability: In stock
  • 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.

  • Author

    Mitchell H. Katz, University of California, San Francisco
    Mitchell H. Katz is Clinical Professor of Medicine, Epidemiology and Biostatistics at the University of California, San Francisco, Attending Physician at the San Francisco General Hospital, and Director of the San Francisco Department of Public Health, San Francisco, USA.

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