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| View Larger Image | Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health) | Hardcoverby Eric Vittinghoff (Author), David V. Glidden (Author), Stephen C. Shiboski (Author), Charles E. McCulloch (Author)
| List Price: | $99.00 | | Price: | $80.19 | | You Save: | $18.81 (19%) | | | Available: | Usually ships in 24 hours |
| | Binding: | Hardcover | | Publisher: | Springer | | Page Count: | 344 Pages | | Publication Date: | June 08, 2007 | | Sales Rank: | 121,368st |
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ACCESSORIES |
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"Implementing an Electronic Health Record System" addresses the range of issues and opportunities that implementing an electronic health records system (EHR) poses for any size of medical organization - from the small one-man operation to a large healthcare system. The book is divided into sections on preparation, support, implementation and a summary and prospects section, enabling the clinician to define the framework necessary to implement and evaluate a clinically effective EHR system....
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| Handbook of Injury and Violence Prevention
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EDITORIAL REVIEWS |
Product Description
This new book provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, repeated-measures models for longitudinal and hierarchical outcomes, and generalized linear models for counts and other outcomes. Treating these topics together takes advantage of all they have in common. The authors point out the many-shared elements in the methods they present for selecting, estimating, checking, and interpreting each of these models. They also show that these regression methods deal with confounding, mediation, and interaction of causal effects in essentially the same way. The examples, analyzed using Stata, are drawn from the biomedical context but generalize to other areas of application. While a first course is statistics is assumed, a chapter reviewing basic statistical methods is included. Some advanced topics are covered but the presentation remains intuitive. A brief introduction to regression analysis of complex surveys and notes for further reading are provided. For many students and researchers learning to use these methods, this one book may be all they need to conduct and interpret multipredictor regression analyses. The authors are on the faculty in the Division of Biostatistics, Department of Epidemiology and Biostatistics, University of California, San Francisco, and are authors or co-authors of more than 200 methodological as well as applied papers in the biological and biomedical sciences. The senior author, Charles E. McCulloch, is head of the Division and author of Generalized Linear Mixed Models (2003), Generalized, Linear, and Mixed Models (2000), and Variance Components (1992). |
CUSTOMER REVIEWS (Average Customer Rating: 4.5 based on 6 reviews)
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Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models by J. Wong  3 Stars
October 17, 2009
Regression Methods in Biostatistics is clearly a very well-organized book, covering topics from simple linear regression theory and methods, to the more complex survival analyses. The material is especially recommended for students who have just completed introductory biostatistics and statistical programming, and are looking for practical applications of their skills (of course, for those looking for more thorough practice, it is recommended that those individuals take more advanced biostatistics courses). Relevant examples are abundant throughout the chapters, and the authors are also very thoughtful in providing a website ([...]) where one is able to download the data (in all types of files) used in all the examples in the book, as well as for the practice problems. One drawback to this book, however, is the authors' reliance on only STATA to present the modeling examples; this is incredibly useful for primarily STATA users (the authors provide tips on STATA codes) but not particularly helpful for SAS users, for example (though it is certainly not a very huge learning barrier).
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Great resource by D. Collingridge (Utah)  5 Stars
June 05, 2008
I have owned this book for a couple of weeks. In that short time it has proven very useful to me.
The authors use an easy-to-follow writing style and don't get too bogged down in theoretical, statistical formulas. It is full of useful figures that illustrate the points being made. Note: although the authors rely on Stata for creating their printouts and figures, this is not a book on how to use Stata. You don't get the feeling that you have to learn Stata in order to follow along. I have found that most of the Stata diagrams are very similar to the diagrams created in SPSS, and probably SAS and R for that matter.
Although I am reading the book from beginning to end, I have already gleaned some useful information from advanced chapters, thus suggesting that it is a good reference book. For instance, I was frustrated by the lack of coverage on interpreting log transformed data (in multiple regression) in other stats books. I was pleased to discover that this book covers this issue in a clear and concise manner. I am also pleased that the authors have included a chapter on generalized linear models.
This is a very good book for people working in health care research. The authors talk to the reader and explain things in a lucid manner (I have read several stats books that do not do this, so it is a refreshing change). The authors also provide many practical examples to clarify the issues. A background in the basics of statistics is required.
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Excellent book by I. RIGAS (Boston, MA) 5 Stars
April 21, 2008
That is exactly what the title promises. High yield introduction to clinically applied regression methods. A marvel of a book for the subject.
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Nice coverage of important topics for biostatisticians by Michael R. Chernick (Holland PA)  4 Stars
November 29, 2007
The authors say that they created this book to fit with a course they taught at UC San Francisco to medical students. The book is very sophisticated and a great reference source for practicing biostatisticians in industry or research. It surprises me a little that they find it effective for there non-technical audience. Although the topics are technical and many are advanced they do cover it in a conceptual way without heavy mathematics but still requiring some statistics classes as prerequisite.
Regression does not cover all the techniques of biostatistics but as the authors point out the four topics in the subtitle are among the most important. I know this from my many years of experience as a bisostatistician in the medical device and pharmaceutical industries. They use many good practical examples useing many of the common variables studies in many clinical trials where physical exams are given to record blood pressure and other vital signs and chemistry labs are done to determine cholesterol levels and other things that can be factors in various diseases. Also glucose levels are very important to monitor for diabetes trials.
In addition to the standard topics general estimating equations and generalized linear models are covered and where appropriate bootstrap confidence intervals. There is even a chapter on complex surveys a topic important when quality of life is an endpoint and survey instruments are used to measure it.
In the survival analysis chapter the Kaplan-Meier curves, log rank tests and Cox proportional hazards models are covered as expected but the authors go further to include extensions of the Cox model when the proportional hazards assumption fails. My only disappointment is that there is no coverage of actuarial life tables. At the medical device companies that I worked for it was common to get interval data on events rather than continuous data and then the Cutler-Ederer life table method is the analog for interval data to the Kaplan-Meier estimator for continuous data.
The book covers many topics but is concise as the authors claim. The authors provide a lot of examples that they work out using the statistical package Stata. The authors claim that Stata is the package of choice for biostatistics. This may be the case in academic settings but is certainly not the case in the pharmaceutical industry where SAS is used almost exclusively. I think that it would have been better to show how to write the computer code for solving these problems both in SAS and Stata. To the authors credit Stat is a very good package for their purpose and they do at times mention SAS and SPSS which are the other two major statistical packages used in industry.
All in all this is a very good book that is worth its list price. I will use it as a reference. it also contains a very nice bibliography of 9 pages.
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very good book, compact but comprehensive by Student_PhD (Madison, WI USA) 5 Stars
May 11, 2007
This book covers a wide range of topics in Biostatistics, in a comprehensive, but not overwhelming way. In my opinion this book has the potential of being useful to a broad audience, from Statisticians to other professionals who do health related research.
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For professional researchers and casual readers alike, STATISTICS WITH STATA opens the door to the full use of the popular Stata program—a fast, flexible, and easy-to-use environment for data management and statistics analysis. Now integrating Stata's impressive new graphics, this comprehensive book presents hundreds of examples showing how you can apply Stata to accomplish a wide variety of tasks. Like Stata itself, STATISTICS WITH STATA will make it easier for you to move fluidly through...
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