- Language: English
- 978 Pages
- Published: July 2015
- Region: Global
Statistics in Medicine. Edition No. 3
- ID: 2089002
- August 2012
- 738 Pages
- Elsevier Science and Technology
Statistics in Medicine, Third Edition makes medical statistics easy to understand by students, practicing physicians, and researchers. The book begins with databases from clinical medicine and uses such data to give multiple worked-out illustrations of every method. The text opens with how to plan studies from conception to publication and what to do with your data, and follows with step-by-step instructions for biostatistical methods from the simplest levels (averages, bar charts) progressively to the more sophisticated methods now being seen in medical articles (multiple regression, noninferiority testing). Examples are given from almost every medical specialty and from dentistry, nursing, pharmacy, and health care management. A preliminary guide is given to tailor sections of the text to various lengths of biostatistical courses.
- User-friendly format includes medical examples, step-by-step methods, and check-yourself exercises appealing to readers with little or no statistical background, across medical and biomedical disciplines
- Facilitates stand-alone methods rather than a required sequence of reading and references to prior text.
- Covers trial randomization, treatment ethics in medical research, imputation of missing data, evidence-based medical decisions, how to interpret medical articles, noninferiority testing, meta-analysis, screening number needed to treat, and epidemiology.
- Fills the gap left in all other medical statistics books between the reader's knowledge of how to go about research and the book's coverage of how to analyze results of that research.
New in this Edition:
- New chapters on planning research, managing data and analysis, Bayesian statistics, measuring association and agreement, and questionnaires and surveys.
- New sections on what tests and descriptive statistics to choose, false discovery rate, interim analysis, bootstrapping, Bland-Altman plots, Markov chain Monte Carlo (MCMC), and Deming regression.
- Expanded coverage on probability, statistical methods and tests relatively new to medical research, ROC curves, experimental design, and survival analysis.
- 35 Databases in Excel format used in the book and can be downloaded and transferred into whatever format is needed along with PowerPoint slides of figures, tables, and graphs from the book included on the companion site, http://www.elsevierdirect.com/companion.jsp?ISBN=9780123848642
- Medical subject index offers additional search capabilities. SHOW LESS READ MORE >
Foreword 3rd Edition
Foreword 2nd Edition
Foreword First Edition
How to Use this Book
1. Planning Studies: From Design to Publication
2. Planning Analysis: What Do I Do with my Data?
3. Probability and Relative Frequency Distributions
5. Descriptive Statistics
6. Finding Probabilities of Error
7. Confidence Intervals
8. Hypothesis Testing: Concept and Practice
9. Tests on Categorical Data
10. Risks, Odds, and ROC Curves
11. Tests on Ranked Data
12. Tests on Means of Continuous Data
13. Multi-Factor ANOVA and ANCOVA
14. Tests on Variability and Distributions
15. Managing Results of Analysis
16. Equivalence Testing
17. Bayesian Statistics
18. Sample Size Estimation and Meta-Analysis
19. Modeling Concepts and Methods
20. Clinical Decisions Based on Models
21. Regression and Correlation
22. Multiple and Curvilinear Regression
23. Survival, Logistic Regression, and Cox Regression
24. Sequential Analysis and Time Series
26. Measuring Association and Agreement
27. Questionnaires and Surveys
28. Methods You Might Meet, But Not Every Day
References and Data Sources
Tables of Probability Distributions
Riffenburgh, Robert H.
Robert H. Riffenburgh, PhD, advises on experimental design, statistical analysis, and scientific integrity of the approximately 400 concurrent studies at the Naval Medical Center San Diego. A fellow of the American Statistical Association and Royal Statistical Society, he is former Professor and Head, Statistics Department, University of Connecticut, and has been faculty at Virginia Tech., University of Hawaii, University of Maryland, University of California San Diego, San Diego State University, and University of Leiden (The Netherlands). He has been president of his own consulting firm and performed and directed operations research for the U.S. government and for NATO. He has consulted on biostatistics throughout his career, has received numerous awards, and has published more than 140 professional articles.