Synopses & Reviews
This versatile textbook allows students and teachers to fashion an instructional package that meets diverse learning needs. It provides a wide-ranging look at basic and advanced biostatistical concepts and methods in a format calibrated to individual interests and levels of proficiency. Each topic presentation features introductory comments, real-life examples, a step-by-step outline of the statistical procedure under discussion, an explanation of applications, and numerous practice exercises. Advanced material-which may be included in coursework at the discretion of the instructor-has been noted throughout the text with asterisks, and notes at the end of each chapter extend and enrich the primary material. Early chapters discuss the design of medical studies, descriptive statistics, and introductory ideas of probability theory and statistical inference. Later chapters explore more advanced statistical methods and illustrate important current uses of biostatistics. Statistical methods discussed include
* Robustness and nonparametric statistics
* Analysis of variance and covariance
* Multiple comparisons
* Discrimination and classification
* Principal component analysis and factor analysis
* Survival analysis (including life tables, product-limit estimates, and Cox proportional hazards regression)
* Sample sizes for observational studies
With more than 390 practice exercises, clear illustrations and graphics, and more than 130 examples, Biostatistics provides a complete detailed seminar, which encourages steady, incremental growth while acting as a catalyst for creative analysis.
Synopsis
A respected introduction to biostatistics, thoroughly updated and revised
The first edition of Biostatistics: A Methodology for the Health Sciences has served professionals and students alike as a leading resource for learning how to apply statistical methods to the biomedical sciences. This substantially revised Second Edition brings the book into the twenty-first century for todays aspiring and practicing medical scientist.
This versatile reference provides a wide-ranging look at basic and advanced biostatistical concepts and methods in a format calibrated to individual interests and levels of proficiency. Written with an eye toward the use of computer applications, the book examines the design of medical studies, descriptive statistics, and introductory ideas of probability theory and statistical inference; explores more advanced statistical methods; and illustrates important current uses of biostatistics.
New to this edition are discussions of
- Longitudinal data analysis
- Randomized clinical trials
- Bayesian statistics
- GEE
- The bootstrap method
Enhanced by a companion Web site providing data sets, selected problems and solutions, and examples from such current topics as HIV/AIDS, this is a thoroughly current, comprehensive introduction to the field.
About the Author
GERALD VAN BELLEis a professor in the Departments of Biostatistics and Environmental and Occupational Health Sciences at the University of Washington in Seattle, Washington. He is the author of four books as well as more than 100 articles and numerous book chapters.
LLOYD D. FISHERis Professor Emeritus in the Department of Biostatistics at the University of Washington, Seattle, and a consultant to the drug and device industries. He has held positions with the Center for AIDS Research, the Mayo Clinic, and the Fred Hutchinson Cancer Center, among others.
PATRICK J. HEAGERTYis an associate professor in the Department of Biostatistics at the University of Washington in Seattle and an associate member at the Fred Hutchinson Cancer Research Center.
THOMAS LUMLEYis an associate professor in the Department of Biostatistics at the University of Washington in Seattle.
Table of Contents
Partial table of contents:
Biostatistical Design of Medical Studies.
Descriptive Statistics.
Statistical Inference: Populations and Samples.
Counting Data.
Categorical Data: Contingency Tables.
Nonparametric, Distribution-Free and Permutation Models: Robust Procedures.
Analysis of Variance.
Association and Prediction: Multiple Regression Analysis, Linear Models with Multiple Predictor Variables.
Multiple Comparisons.
Discrimination and Classification.
Rates and Proportions.
Analysis of the Time to an Event: Survival Analysis.
Sample Sizes for Observational Studies.
A Personal Postscript.
Appendix.
Indexes.