Average Reviews:
(More customer reviews)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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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).
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