Logistic Regression: A Self-Learning Text [Hardcover]

David G. Kleinbaum (Author), Mitchel Klein (Author)

Book Description

Publication Date: August 12, 2002 | ISBN-10: 0387953973 | ISBN-13: 978-0387953977 | Edition: 2nd

This is the second edition of this text on logistic regression methods. As in the first edition, each chapter contains a presentation of its topic in "lecture-book" format together with objectives, an outline, key formulae, practice exercises, and a test. The "lecture-book" has a sequence of illustrations and formulae in the left column of each page and a script (i.e., text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented. This second edition includes five new chapters and an appendix. The new chapters are: Chapter 9. Polytomous Logistic Regression Chapter 10. Ordinal Logistic Regression Chapter 11. Logistic Regression for Correlated Data Chapter 12. GEE Examples Chapter 13. Other Approaches for Analysis of Correlated Data Chapters 9 and 10 extend logistic regression to response variables that have more than two categories. Chapters 11-13 extend logistic regression to generalized estimating equations (GEE) and other methods for analyzing correlated response data. The appendix "Computer Programs for Logistic Regression" provides descriptions and examples of computer programs for carrying out the variety of logistic regression procedures described in the main text. The software packages considered are SAS Version 8.0, SPSS Version 10.0 and STATA Version 7.0.

Editorial Reviews

Review

From the reviews of the third edition: “The third edition of this book continues the tradition of the authors of a two-column book that really does act as a self-learning text. The left-hand column is like a collection of PowerPoint slides, including generic-style computer output and diagrams to visualize the relationship between concepts. Each chapter contains about 10 exercises, some routine calculation and some asking for explanation of particular points. Answers are provided immediately. … The reference list includes about 40 items and has been updated to include publications up to 2008.” (Alice Richardson, International Statistical Review, Vol. 79 (2), 2011) --This text refers to an alternate Hardcover edition.

From the Back Cover

This very popular textbook is now in its third edition. Whether students or working professionals, readers appreciate its unique "lecture book" format. They often say the book reads like they are listening to an outstanding lecturer. This edition includes three new chapters, an updated computer appendix, and an expanded section about modeling guidelines that consider causal diagrams. Like previous editions, this textbook provides a highly readable description of fundamental and more advanced concepts and methods of logistic regression. It is suitable for researchers and statisticians in medical and other life sciences as well as academicians teaching second-level regression methods courses. The new chapters are: • Additional Modeling Strategy Issues, including strategy with several exposures, screening variables, collinearity, influential observations and multiple-testing • Assessing Goodness to Fit for Logistic Regression • Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves The Computer Appendix provides step-by-step instructions for using STATA (version 10.0), SAS (version 9.2), and SPSS (version 16) for procedures described in the main text. David Kleinbaum is Professor of Epidemiology at Emory University Rollins School of Public Health in Atlanta, Georgia. Dr. Kleinbaum is internationally known for his innovative textbooks and teaching on epidemiological methods, multiple linear regression, logistic regression, and survival analysis. He has taught more than 200 courses worldwide. The recipient of numerous teaching awards, he received the first Association of Schools of Public Health Pfizer Award for Distinguished Career Teaching in 2005. Mitchel Klein is Research Assistant Professor with a joint appointment in the Environmental and Occupational Health Department and the Epidemiology Department at Emory University Rollins School of Public Health. He has successfully designed and taught epidemiologic methods physicians at Emory’s Master of Science in Clinical Research Program. Dr. Klein is co-author with Dr. Kleinbaum of the second edition of Survival Analysis-A Self-Learning Text. --This text refers to an alternate Hardcover edition.

Product Details

• Hardcover: 536 pages
• Publisher: Springer; 2nd edition (August 12, 2002)
• Language: English
• ISBN-10: 0387953973
• ISBN-13: 978-0387953977
• Product Dimensions: 9.7 x 8.1 x 1.2 inches
• Shipping Weight: 2.7 pounds (View shipping rates and policies)
• Average Customer Review: 4.6 out of 5 stars  See all reviews (11 customer reviews)
• Amazon Best Sellers Rank: #72,989 in Books (See Top 100 in Books)
  • #30 in Books > Medical Books > Basic Sciences > Biostatistics
  • #69 in Books > Medical Books > Research

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Customer Reviews

Most Helpful Customer Reviews

29 of 30 people found the following review helpful:

5.0 out of 5 stars An excellent step-by-step text, March 23, 2001

By 

Mme Viallefont Anne (Lyon, France) - See all my reviews

This review is from: Logistic Regression: A Self-Learning Text (Hardcover)

When Kleinbaum entitles his book "a self-learning text", this is TRUE ! I'm sure anyone can learn logistic regression with this book. It is cristal-clear, very progressive, with real-data examples... If the best teachers are those who make you feel you're intelligent, certainly the author must be a good teacher... because his book is ! I do recommend it warmly to anyone who has to teach (like me) or learn logistic regression.

19 of 19 people found the following review helpful:

4.0 out of 5 stars depends on what background you are coming from..., September 9, 2004

By 

trilungdoc "trilungdoc" (trilungdoc) - See all my reviews

This review is from: Logistic Regression: A Self-Learning Text (Hardcover)

I'm a physician learning about clinical research/biostatistics etc. I found that this book was extremely helpful in guiding me through basic rules, steps and theories on how to build a logistic regression model. The examples where straight forward, even for a person without a strong math background. However, I can also see that this would not be enough for a person set out to be a biostatistician, as this book would seem rather elementary. If you are a person with a so-so background in math and statistics, and are interested in learning to adequately perform statistical analyses with logistic regression, this is the book for you.

20 of 21 people found the following review helpful:

4.0 out of 5 stars Good for what it is, January 5, 2004

By 

wiredweird "wiredweird" (Earth, or somewhere nearby) - See all my reviews
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This review is from: Logistic Regression: A Self-Learning Text (Hardcover)

This book has a specific goal. It's aim is to give a basic competence in the use of logistic regression, related techniques, and the software that deal with them. This, it does very well. By intent, it leaves many other needs unmet.

The format is 13 chapters, possibly representing the 13 or 14 weeks in a typical school term. Each chapter has a specific statement of teaching goals at the front, a summary outline of the course to date in the back, and a few pages of questions or exercises with answers. There appear to be sample data sets available, formatted for popular stats packages, but I did not figure out how they are made available. Within the main text of each chapter, every page reads like a blackboard lecture: equations on the left and narration on the right. The presentation uses a minimum of math, just a little algebra and exponentials in a few specific forms.

For the aspiring tool-user, this book may be worth a semester's tuition. I can fault it only for an annoying habit of writing out in words equations that appear on the same page ("e raised to the power of the sum of products ... ").

This book is NOT meant for people truly interested in the theory or practice of the exact computations. For example, its use of probability scarely mentions joint or conditional distributions. As a result, some of its formulas (e.g. p.48) come across as rote memorization, instead of natural expressions of the laws of probability. Lacking joint probability, the covariance matrix can not have meaning. It is just something produced, somehow, by an oracular computer program.

The repeated phrase, "according to statisticians ..." makes it very clear that statisticians are a breed distinct from intended audience. What they do is quite alien, but somehow, sometimes leaves the student with formulas to grind through.

Before you buy this book, be very clear about what you expect from it. Beginning students may get a lot from it. Readers already familiar with probability and some stats are likely to be disappointed.