Biostatistical Methods: The Assessment of Relative Risks (Wiley Series in Probability and Statistics)

Book Description

Comprehensive coverage of classical and modern methods of biostatistics

Biostatistical Methods focuses on the assessment of risks and relative risks on the basis of clinical investigations. It develops basic concepts and derives biostatistical methods through both the application of classical mathematical statistical tools and more modern likelihood-based theories.

The first half of the book presents methods for the analysis of single and multiple 2x2 tables for cross-sectional, prospective, and retrospective (case-control) sampling, with and without matching using fixed and two-stage random effects models. The text then moves on to present a more modern likelihood- or model-based approach, which includes unconditional and conditional logistic regression; the analysis of count data and the Poisson regression model; and the analysis of event time data, including the proportional hazards and multiplicative intensity models. The book contains a technical appendix that presents the core mathematical statistical theory used for the development of classical and modern statistical methods. Biostatistical Methods: The Assessment of Relative Risks:
* Presents modern biostatistical methods that are generalizations of the classical methods discussed
* Emphasizes derivations, not just cookbook methods
* Provides copious reference citations for further reading
* Includes extensive problem sets
* Employs case studies to illustrate application of methods
* Illustrates all methods using the Statistical Analysis System(r) (SAS)

Supplemented with numerous graphs, charts, and tables as well as a Web site for larger data sets and exercises, Biostatistical Methods: The Assessment of Relative Risks is an excellent guide for graduate-level students in biostatistics and an invaluable reference for biostatisticians, applied statisticians, and epidemiologists.

Customer Reviews:

modern coverage emphasizing relative risk.......2000-09-20

John Lachin is Professor and Director of the graduate program in biostatistics at George Washington University. The book is intended as a first advanced course for students in that program. The book emphasizes methods for problems in biostatistics. To Lachin this means an emphasis on binary, categorical and survival data that relate to the assessment of risk and relative risk through clinical research. Consequently much of the standard parametric and nonparametric modeling of continuous response data is not considered.

A variety of methods are covered on a number of subjects. The first half of the book deals with classical approaches to single and multiple 2x2 contigency tables used in cross-sectional, prospective and case-control studies. In the second half, the more modern likelihood or model-based approach is presented. Technical mathematical details are covered in the appendix which is referenced throughout the text. The appendix deals with statistical theory (stochastic convergence results and other theory) but does not provide rigorous proofs of the theorems. Real probelms are presented and analyses are illustrated using procedures in SAS.

Book Description

An introduction to classical biostatistical methods in epidemiology

Biostatistical Methods in Epidemiology provides an introduction to a wide range of methods used to analyze epidemiologic data, with a focus on nonregression techniques. The text includes an extensive discussion of measurement issues in epidemiology, especially confounding. Maximum likelihood, Mantel-Haenszel, and weighted least squares methods are presented for the analysis of closed cohort and case-control data. Kaplan-Meier and Poisson methods are described for the analysis of censored survival data. A justification for using odds ratio methods in case-control studies is provided. Standardization of rates is discussed and the construction of ordinary, multiple decrement and cause-deleted life tables is outlined. Sample size formulas are given for a range of epidemiologic study designs. The text ends with a brief overview of logistic and Cox regression. Other highlights include:

Many worked examples based on actual data
Discussion of exact methods
Recommendations for preferred methods
Extensive appendices and references

Biostatistical Methods in Epidemiology provides an excellent introduction to the subject for students, while also serving as a comprehensive reference for epidemiologists and other health professionals.
For more information, visit www.wiley.com/mathematics

Download Description

An introduction to classical biostatistical methods in epidemiology
Biostatistical Methods in Epidemiology provides an introduction to a wide range of methods used to analyze epidemiologic data, with a focus on nonregression techniques. The text includes an extensive discussion of measurement issues in epidemiology, especially confounding. Maximum likelihood, Mantel-Haenszel, and weighted least squares methods are presented for the analysis of closed cohort and case-control data. Kaplan-Meier and Poisson methods are described for the analysis of censored survival data. A justification for using odds ratio methods in case-control studies is provided. Standardization of rates is discussed and the construction of ordinary, multiple decrement and cause-deleted life tables is outlined. Sample size formulas are given for a range of epidemiologic study designs. The text ends with a brief overview of logistic and Cox regression. Other highlights include:
* Many worked examples based on actual data
* Discussion of exact methods
* Recommendations for preferred methods
* Extensive appendices and references
Biostatistical Methods in Epidemiology provides an excellent introduction to the subject for students, while also serving as a comprehensive reference for epidemiologists and other health professionals.
For more information, visit www.wiley.com/mathematics

Customer Reviews:

Interesting and difficult book.......2005-01-09

This was a difficult book for me to read and review and I think it would be tough to write as well. Biostatistics is taught generally devoid of its epidemiological context but this book tries to break a new ground and tries to show use of biostatistics in derivations of epidemiology measures. This makes the book theoritical which is what I was looking for. It succesfully breaks a new ground in some places particularly on on confounding where author summarizes recent literature neatly. However, other chapters require knowledge of Maximum Likelihood (ML) methods which are covered in an intermediate biostatistic texts like Larsen and Marx. This book is interesting because its the only book which I have found which covers the ML methods for odds ratio but this is the only book which covers them. Disappointingly, the author skips derivations and reaches straight to the results which is frustating. Since this is textbook for advanced students and author assumes knowledge of ML methods, eschewing details and derivations is sad.

Modern Applied Biostatistical Methods: Using S-Plus

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Modern Applied Biostatistical Methods: Using S-Plus
Steve Selvin
Manufacturer: Oxford University Press, USA
ProductGroup: Book
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Warning: Text contains many errors.......2007-02-17

+ Note: This review was originally written for another book from this author, but touches on my experience with this text as well.

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