Biostatistics

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 today's
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.

Autorentext

GERALD VAN BELLE is 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. FISHER is 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. HEAGERTY is 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 LUMLEY is an associate professor in the Department
of Biostatistics at the University of Washington in Seattle.

Zusammenfassung
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 today's 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.

Inhalt

Preface to the First Edition.

Preface to the Second Edition.

1. Introduction to Biostatistics.

2. Biostatistical Design of Medical Studies.

3. Descriptive Statistics.

4. Statistical Inference: Populations and Samples.

5. One- and Two-Sample Inference.

6. Counting Data.

7. Categorical Data: Contingency Tables.

8. Nonparametric, Distribution-Free and Permutation Models:
   Robust Procedures.

9. Association and Prediction: Linear Models with One Predictor
   Variable.

10. Analysis of Variance.

11. Association and Prediction: Multiple Regression Analysis,
    Linear Models with Multiple Predictor Variables.

12. Multiple Comparisons.

13. Discrimination and Classification.

14. Principal Component Analysis and Factor Analysis.

15. Rates and Proportions.

16. Analysis of the Time to an Event: Survival Analysis.

17. Sample Sizes for Observational Studies.

18. Longitudinal Data Analysis.

19. Randomized Clinical Trials.

20. A Personal Postscript.

Appendix.

Author Index.

Subject Index.

Symbol Index.