Principles of Biostatistics

This book is a concepts-based introduction to statistical procedures that prepares public health, medical, and life sciences students to conduct and evaluate research. Thoroughly revised, this third edition includes a new chapter introducing the basic principles of Study Design.

Auteur

Marcello Pagano is Professor of Statistical Computing in the Department of Biostatistics at the Harvard School of Public Health. His research in biostatistics is on computer intensive inference and surveillance methods that involve screening methodologies, with their associated laboratory tests, and in obtaining more accurate testing results that use existing technologies.

Kimberlee Gauvreau is Associate Professor in the Department of Biostatistics and Associate Professor of Pediatrics at Harvard Medical School. Dr. Gauvreau's research focuses on biostatistical issues arising in the field of pediatric cardiology. She also works on the development and validation of methods of adjustment for case mix complexity. Heather Mattie is Lecturer on Biostatistics and the Co-Director Health Data Science Program at the Harvard School of Public Health.

Texte du rabat

Principles of Biostatistics, Third Edition is a concepts-based introduction to statistical procedures that prepares public health, medical, and life sciences students to conduct and evaluate research. With an engaging writing style and helpful graphics, the emphasis is on concepts over formulas or rote memorization. Throughout the book, the authors use practical, interesting examples with real data to bring the material to life. Thoroughly revised and updated, this third edition includes a new chapter introducing the basic principles of Study Design, as well as new sections on sample size calculations for two-sample tests on means and proportions, the Kruskal-Wallis test, and the Cox proportional hazards model. It also includes additional review exercises in each chapter.

Key Features:

• Includes interesting examples with real data.

• Uses Stata and R.

• Datasets and Stata and R code are available on the book's website.

The book is divided into three parts. The first five chapters deal with collections of numbers and ways in which to summarize, explore, and explain them. The next two chapters focus on probability and introduce the tools needed for the subsequent investigation of uncertainty. It is only in the eighth chapter and thereafter that the authors distinguish between populations and samples and begin to investigate the inherent variability introduced by sampling, thus progressing to inference. Postponing the slightly more difficult concepts until a solid foundation has been established makes it easier for the reader to comprehend them.

Contenu

1. Introduction

Part I Variability

2. Descriptive Statistics

3. Rates and Standardization

4. Life Tables

Part II Probability

5. Probability

6. Screening and Diagnostic Tests

7. Theoretical Probability Distributions

8. Sampling Distribution of the Mean

Part III Inference

9. Confidence Intervals

10. Hypothesis Testing

11. Comparison of Two Means

12. Analysis of Variance

13. Nonparametric Methods

14. Inference on Proportions

15. Contingency Tables

16. Correlation

17. Simple Linear Regression

18. Multiple Linear Regression

19. Logistic Regression

20. Survival Analysis

21. Sampling Theory

22. Study Design

Bibliography

Glossary

Statistical Tables

Index