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商品の説明
内容説明
Praise for the First Edition
". . . an excellent textbook . . . an indispensable reference for biostatisticians and epidemiologists."
International Statistical Institute
A new edition of the definitive guide to classical and modern methods of biostatistics
Biostatistics consists of various quantitative techniques that are essential to the description and evaluation of relationships among biologic and medical phenomena. Biostatistical Methods: The Assessment of Relative Risks, Second Edition develops basic concepts and derives an expanded array of biostatistical methods through the application of both classical statistical tools and more modern likelihood-based theories. With its fluid and balanced presentation, the book guides readers through the important statistical methods for the assessment of absolute and relative risks in epidemiologic studies and clinical trials with categorical, count, and event-time data.
Presenting a broad scope of coverage and the latest research on the topic, the author begins with categorical data analysis methods for cross-sectional, prospective, and retrospective studies of binary, polychotomous, and ordinal data. Subsequent chapters present modern model-based approaches that include unconditional and conditional logistic regression; Poisson and negative binomial models for count data; and the analysis of event-time data including the Cox proportional hazards model and its generalizations. The book now includes an introduction to mixed models with fixed and random effects as well as expanded methods for evaluation of sample size and power. Additional new topics featured in this Second Edition include:
- Establishing equivalence and non-inferiority
- Methods for the analysis of polychotomous and ordinal data, including matched data and the Kappa agreement index
- Multinomial logistic for polychotomous data and proportional odds models for ordinal data
- Negative binomial models for count data as an alternative to the Poisson model
- GEE models for the analysis of longitudinal repeated measures and multivariate observations
Throughout the book, SAS is utilized to illustrate applications to numerous real-world examples and case studies. A related website features all the data used in examples and problem sets along with the author's SAS routines.
Biostatistical Methods, Second Edition is an excellent book for biostatistics courses at the graduate level. It is also an invaluable reference for biostatisticians, applied statisticians, and epidemiologists.
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.
--このテキストは、 ハードカバー 版に関連付けられています。
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.
--このテキストは、 ハードカバー 版に関連付けられています。
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27 人中、27人の方が、｢このレビューが参考になった｣と投票しています。
excellent text emphasizing relative risk
2008/2/8
形式:ハードカバー
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.
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.
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.
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