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Download Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health) eBook

by Eric Vittinghoff,David V. Glidden,Charles E. McCulloch,Stephen C. Shiboski

Download Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Statistics for Biology and Health) eBook
ISBN:
1461413524
Author:
Eric Vittinghoff,David V. Glidden,Charles E. McCulloch,Stephen C. Shiboski
Category:
Biological Sciences
Language:
English
Publisher:
Springer; 2nd ed. 2012 edition (September 1, 2011)
Pages:
512 pages
EPUB book:
1826 kb
FB2 book:
1725 kb
DJVU:
1451 kb
Other formats
mobi docx lrf txt
Rating:
4.6
Votes:
651


The authors have written a very readable book focusing on the most widely used regression models in biostatistics: Multiple linear regression, logistic regression and Cox regression.

The authors have written a very readable book focusing on the most widely used regression models in biostatistics: Multiple linear regression, logistic regression and Cox regression. The book is written for a non-statistical audience, focusing on ideas and how to interpret result. .The book will b. seful as a reference to give to a non-statistical colleagu.

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. 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).

Eric Vittinghoff, David Glidden, Steve Shiboski, Charles E. McCulloch. 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).

by Eric Vittinghoff & David Glidden & Steve Shiboski & Charles E. parametric survival regression models and the Cox semi parametric survival model. Although the book can be read. parametric survival regression models and the Cox semi parametric survival model Selected healing herbs of Himalaya: a pictorial & herbaria guide. 26 MB·25,311 Downloads·New!. Statistics and probability for engineering applications with Microsoft Excel. Stochastic equations through the eye of the physicist basic concepts, exact results and asymptotic approximations. 81 MB·15,664 Downloads·New!

Stephen C. Shiboski Charles E.

Stephen C. Regression Methods in Biostatistics Linear, Logistic, Survival, and Repeated Measures Models With 54 Illustrations.

Эту книгу можно прочитать в Google. McCulloch

Eric Vittinghoff, David Glidden, Steve Shiboski, Charles E. Here is a unified, readable introduction to multipredictor regression methods in biostatistics, including linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored survival times, and generalized linear models for counts and other outcomes

Daniel Voss, PhD, is Professor Emeritus of Mathematics and Statistics and former Interim Dean of the College of Science and Mathematics at Wright State University, Dayton, Ohio.

Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Eric Vittinghoff, David Glidden, Steve Shiboski, Charles E. McCulloch). 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63.

Eric Vittinghoff Stephen C. Shiboski David V. Glidden Charles E. McCulloch Regression Methods in.Looking beyond the clustering and repeated measures (which are covered in Chap

Eric Vittinghoff Stephen C. McCulloch Regression Methods in Biostatistics Linear, Logistic, Survival, and Repeated Measures Models With 54 Illustrations. Looking beyond the clustering and repeated measures (which are covered in Chap. 8), what if physicians with more aggressive approaches to back pain also tended to have older patients? If older patients recover more slowly (re- gardless of treatment), then even if differences in treatment aggressiveness.

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 in 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.

  • Yramede
Overall a very excellent, broad yet detailed overview of regression and statistical methods for parsing meaning and substance from different epidemiologic and/or other health-related investigations. One caveat: the writing is extremely verbose and geared toward analytic, mathematical parsing of meaning in context of data graphical overlays. Can be understood by any functional graduate student with robust quantitative skills, but is still a bit awkward/stilted in how the information is conveyed with numbering of tables, graphs, etc., in reference to textual explanations. Other than that, kudos. Very helpful.
  • Welen
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.
  • Stan
You can actually read this book - which is surprising given the subject. I'm a grad student taking two Biostats courses for a master's degree. This book is great and conceptual.
  • Alexandra
Vittinghoff is very verbose in explanations of the methods within, but this is very useful to newcomers in the field. The examples are robust and coded in a number of common statistical programming environments.
  • Faezahn
One of the best books for beginners. The text has good examples and detailed explanations
  • Ylonean
The Kindle version struggles with the formatting of math equations and isn't much cheaper (albeit more convenient) than the hard copy. I would seriously consider ordering the actual book if I hadn't already purchased the Kindle version.
  • Doukasa
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).
Useful book, but hard to read. The writing often requires you to re-read passages before you can understand what the authors mean. This book is a bible, and necessary, but it would benefit from some editing.