Multivariable Analysis; A Practical Guide for Clinicians / Edition 1

Multivariable Analysis; A Practical Guide for Clinicians / Edition 1

ISBN-10:
0521596939
ISBN-13:
9780521596930
Pub. Date:
05/01/1999
Publisher:
Cambridge University Press

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Overview

Multivariable Analysis; A Practical Guide for Clinicians / Edition 1

Multivariable analysis is a challenging subject for clinicians, whether they are novice researchers or trained practitioners. Most basic biostatistics books do not cover multivariable analysis, while existing multivariable analysis books are dense with mathematical formulas. Multivariable Analysis: A Practical Guide for Clinicians steps aside from mathematics and offers conceptual explanations. Dr. Mitchell Katz follows a nonthreatening, question-and-answer approach to explain how to perform and interpret multivariable analyses. He begins by explaining why clinicians should do multivariable analyses and then guides the reader through topics such as how to choose which type of multivariable method to perform, how to deal with missing data, and how to validate multivariable models. The book is loaded with useful tips, tables, figures, and references. Examples from the medical literature demonstrate several real-world applications and uses of multivariable analysis. This book will prove to be an indispensable guide for medical students, residents, and practicing physicians.

Product Details

ISBN-13: 9780521596930
Publisher: Cambridge University Press
Publication date: 05/01/1999
Edition description: Older Edition
Pages: 192
Product dimensions: 6.14(w) x 9.21(h) x 0.83(d)

Table of Contents

1. Introduction; 2. Common uses of multivariable models; 3. Outcome variables in multivariable analysis; 4. Independent variables in multivariable analysis; 5. Assumptions of multiple linear regression, logistic regression, and proportional hazard analysis; 6. Relationship of independent variables to one another; 7. Setting up a multivariable analysis: subjects; 8. Performing the analysis; 9. Interpreting the analysis; 10. Checking the assumptions of the analysis; 11. Validation of models; 12. Special topics; 13. Publishing your study; 14. Summary: steps for constructing a multivariable model.

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