Statistics in Medicine
- 5th Edition - April 1, 2026
- Latest edition
- Authors: Robert H. Riffenburgh, Daniel L. Gillen
- Language: English
- Hardback ISBN:9 7 8 - 0 - 4 4 3 - 4 9 0 8 8 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 4 9 0 8 9 - 7
"Statistics in Medicine, 5th Edition" serves as an essential resource for health care students, professionals and researchers seeking to understand the application of statistic… Read more
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"Statistics in Medicine, 5th Edition" serves as an essential resource for health care students, professionals and researchers seeking to understand the application of statistical methods in medical research. This comprehensive text encompasses a wide range of topics, from foundational concepts to advanced techniques, ensuring that readers are well-equipped to design, analyse, and interpret health-related studies. The book includes updated chapters on critical subjects such as missing data, regression models for discrete outcomes, and the integration of machine learning and AI with statistical methodologies. Each chapter provides practical examples and step-by-step methodologies, enhancing the reader's understanding of complex concepts while reinforcing learning through exercises and real-world applications. For the academic audience, this book offers a user-friendly approach to medical statistics, making it accessible even to those with limited statistical training. By bridging the gap between theory and practice, it empowers health care professionals to conduct rigorous research, interpret findings accurately, and contribute meaningfully to advancements in medical science.
• Provides an extensive overview of statistical methods relevant to medical research, ensuring that readers grasp fundamental concepts and advanced techniques crucial for designing, analyzing, and interpreting studies in health care
• Incorporates medical examples, step-by-step methodologies, and check-yourself exercises, making complex statistical concepts accessible to readers with minimal statistical background, thereby facilitating effective learning and application
• Introduces new chapters on contemporary topics such as missing data, regression models for discrete outcomes, and the interplay between statistics, machine learning, and AI, addressing the evolving landscape of medical statistics and research methodologies
• Incorporates medical examples, step-by-step methodologies, and check-yourself exercises, making complex statistical concepts accessible to readers with minimal statistical background, thereby facilitating effective learning and application
• Introduces new chapters on contemporary topics such as missing data, regression models for discrete outcomes, and the interplay between statistics, machine learning, and AI, addressing the evolving landscape of medical statistics and research methodologies
Undergraduate and Graduate Students, Health Care Professionals and Researchers and Clinicians in fields related to health care, biostatistics, epidemiology, and medical research who require a foundational understanding of statistical methods.
FOREWORDS ACKNOWLEDGMENTS HOW TO USE THIS BOOK
1. Planning Studies: From Design to Publication
2. Planning Analysis: How to Reach My Scientific Objective
3. Probability and Relative Frequency
4. Distributions.
5. Descriptive Statistics.
6. Finding Probabilities.
7. Hypothesis Testing: Concept and Practice.
8. Tolerance, Prediction, and 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 and Multi-Factor ANOVA.
17. Regression Models for Discrete Outcomes.
18. Polytomous Response Regression.
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. Missing Data.
24. Meta Analyses.
25. Tree-Based Methods.
26. Bayesian Statistics.
27. Questionnaires and Surveys.
28. Techniques to Aid Analysis.
29. Data Science, Statistics, Machine Learning and AI.
30. Methods You Might Meet, But Not Every Day.
ANSWERS TO CHAPTER EXERCISES DATABASES TABLES OF PROBABILITY DISTRIBUTIONS SYMBOL INDEX STATISTICAL SUBJECT INDEX MEDICAL SUBJECT INDEX
1. Planning Studies: From Design to Publication
2. Planning Analysis: How to Reach My Scientific Objective
3. Probability and Relative Frequency
4. Distributions.
5. Descriptive Statistics.
6. Finding Probabilities.
7. Hypothesis Testing: Concept and Practice.
8. Tolerance, Prediction, and 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 and Multi-Factor ANOVA.
17. Regression Models for Discrete Outcomes.
18. Polytomous Response Regression.
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. Missing Data.
24. Meta Analyses.
25. Tree-Based Methods.
26. Bayesian Statistics.
27. Questionnaires and Surveys.
28. Techniques to Aid Analysis.
29. Data Science, Statistics, Machine Learning and AI.
30. Methods You Might Meet, But Not Every Day.
ANSWERS TO CHAPTER EXERCISES DATABASES TABLES OF PROBABILITY DISTRIBUTIONS SYMBOL INDEX STATISTICAL SUBJECT INDEX MEDICAL SUBJECT INDEX
- Edition: 5
- Latest edition
- Published: April 1, 2026
- Language: English
RR
Robert H. Riffenburgh
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.
Affiliations and expertise
Naval Medical Center, San Diego, California, USADG
Daniel L. Gillen
Daniel L. Gillen, PhD, is Professor and Chair of Statistics at University of California, Irvine (UCI). He is Fellow of the American Statistical Association and Past President of the Western North American Region of the International Biometric Society. He leads the Data and Statistics Core for the Alzheimer’s Disease Research Center at UCI and is the former head of the Biostatistics Shared Resource at the UCI Chao Family Cancer Center. He serves as a consultant to the FDA and the biopharmaceutical industry and has served on over 30 independent safety monitory boards for multi-center international clinical trials. He has published over 160 peer-reviewed articles in statistical methods and clinical science journals.
Affiliations and expertise
Professor and Chair, Department of Statistics, Program in Public Health, and Department of Epidemiology, University of California, Irvine, USA