From Book News, Inc.
Lachin (statistics, George Washington U.) presents the foundations of biostatistics methodologies, which he feels is different from convention statistics in its focus on the assessment of risks and relative risks through clinical research. After developing basic concepts and deriving core methods through the application of classical mathematical statistical tools, he attempts to show that these and comparable methods may also be developed through the application of more modern likelihood-based theories. Specific topics include conditional logistic regression, the analysis of count data and the Poisson regression, and the analysis of event time data including the proportional hazards and multiplicative intensity models.Book News, Inc.®, Portland, OR
Review
“…recommended for graduate students and researchers in biostatistics and epidemiology…” (Statistical Methods in Medical Research, No.13, 2004) This is an excellent textbook for an advanced course in biostatsitics and also an indispensable reference for biostatisticians and epidemiologists...what makes this textbook so valuable is that it covers the core methods first using classical statistical tools and then likelihood-based theories, highlighting the continuities. Another important feature is the care and balance with which it is drafted: the reasoning is always clear, the mathematical presentation detailed but to thepoint, the examples linked across different chapters. (Short Book Reviews, Vol. 20, No. 3, December 2000) "...an excellent guide for graduate-level students in biostatistics and invaluable reference for biostatisticians, applied statisticians, and epidemiologists." (Mathematical Reviews, Issue 2001h) "...does a very thorough job of establishing a sound bias for the use of biostatistical methodology." (Technometrics, February 2002) "..an excellent guide for graduate-level students in biostatistics and an invaluable reference for biostatisticians, applied statisticians, and epidemiologists." (Zentralblatt MATH, Vol. 961, 2001/11) "the book is an excellent guide" (Zentralblatt MATH, Vol.961, No.11 2001)
Review
“…recommended for graduate students and researchers in biostatistics and epidemiology…” (Statistical Methods in Medical Research, No.13, 2004) "...an excellent guide for graduate-level students in biostatistics and invaluable reference for biostatisticians, applied statisticians, and epidemiologists." (Mathematical Reviews, Issue 2001h) "...does a very thorough job of establishing a sound bias for the use of biostatistical methodology." (Technometrics, February 2002) "..an excellent guide for graduate-level students in biostatistics and an invaluable reference for biostatisticians, applied statisticians, and epidemiologists." (Zentralblatt MATH, Vol. 961, 2001/11) "the book is an excellent guide" (Zentralblatt MATH, Vol.961, No.11 2001)
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.
Book Info
Focuses on the assessment of risks and relative risks on the basis of clinical investigations. Supplemented with numerous graphs, charts, and tables as well as a Web site for larger data sets and exercises.
From the Back Cover
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® (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.
About the Author
JOHN M. LACHIN, ScD, is Professor of Statistics and Biostatistics at the George Washington University in Washington, D.C., and Director of the Biostatistics Center in Rockville, Maryland. Rating 5.0
modern coverage emphasizing relative riskJohn 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. In the model-based sections, topics include logistic regression, Poisson regression, proportional hazard and multiplicative intensity models. The book is modern, well written, provides a good list of references, has extensive problem sets at the end of the chapters and employs case studies to illustrate the application of the methods. It is not a book for beginners. It is a great reference source for biostatisticians and epidemiologists as well as a fine text for a graduate-level course in biostatistics. |
