Statistics in Medicine, Fourth Edition, helps medical and biomedical investigators design and answer questions about analyzing and interpreting data and predicting the sample size required to achieve useful results. It makes medical statistics easy for the non-biostatistician by outlining common methods used in 90% of medical research. The text covers how to plan studies from conception to publication, what to do with data, and follows with step-by-step instructions for biostatistical methods from the simplest levels, to more sophisticated methods now used in medical articles. Examples from almost every medical specialty, and from dentistry, nursing, pharmacy and health care management are provided.
This book does not require background knowledge of statistics or mathematics beyond high school algebra and provides abundant clinical examples and exercises to reinforce concepts. It is a valuable source for biomedical researchers, healthcare providers and anyone who conducts research or quality improvement projects.
- Expands and revises important topics, such as basic concepts behind descriptive statistics and testing, descriptive statistics in three dimensions, the relationship between statistical testing and confidence intervals, and more
- Presents an easy-to-follow format with medical examples, step-by-step methods and check-yourself exercises
- Explains statistics for users with little statistical and mathematical background
- Encompasses all research development stages, from conceiving a study, planning it in detail, carrying out the methods, putting obtained data in analyzable form, analyzing and interpreting the results, and publishing the study
1. Planning Studies: From Design to Publication 2. Planning Analysis: Addressing Your Scientific Objective 3. Probability and Relative Frequency 4. Distributions 5. Descriptive Statistics 6. Finding Probabilities 7. Hypothesis Testing: Concept and Practice 8. Confidence Intervals 9. Tests on Categorical Data 10. Risks, Odds, and ROC Curves 11. Tests of Location with Continuous Outcomes 12. Equivalence Testing 13. Tests on Variability and Distributions 14. Measuring Association and Agreement 15. Linear Regression and Correlation 16. Multiple Linear and Curvilinear Regression 17. Logistic Regression for Binary Outcomes 18. Regression Models for Count Outcomes 19. Analysis of Censored Time-To-Event Data 20. Analysis of Repeated Continuous Measures of Time 21. Sample Size Estimation 22. Clinical Trials and Group Sequential Analyses 23. Epidemiology and Alternative Sampling Designs 24. Meta Analyses 25. Bayesian Statistics 26. Questionnaires and Surveys 27. Techniques to Aid Analysis 28. Methods You Might Meet, But Not Every Day
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.
Gillen, Daniel L.
Daniel L. Gillen's research focuses on the development of statistical methodology for censored survival data, group sequential methods for the design and analysis of clinical trials, and the analysis of longitudinal data. As a general rule, his methodologic research is motivated by applications stemming from a multitude of clinical disciplines. Some of his most recent academic distinctions: Fellow of the American Statistical Association (Elected 2016); Dean's Award for Mid-career Research Excellence, UCI School of Information and Computer Science (2015); Dean's Award for Service Excellence, UCI School of Information and Computer Science (2012); Excellence in Mentoring Award, UCI Institute for Clinical and Translational Sciences (2011); Donovan J. Thompson Award, Department of Biostatistics, University of Washington (2000); NIH Cancer-Epidemiology-Biostatistics Training Grant (1999 - 2003). He is Associate Editor, Case Studies and Applications, of Journal of The American Statistical Association (2009 - present), and was Associate Editor of Journal of Statistical Software (2012 - 2015).