Biostatistics: A Methodology For the Health Sciences, 2nd Edition

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thumbnail image: Biostatistics: A Methodology For the Health Sciences, 2nd Edition
  • Published: 03 August 2004
  • ISBN: 0471031852
  • Author(s): Gerald van Belle, Lloyd D. Fisher, Patrick J. Heagerty, Thomas Lumley
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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.

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.

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