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Elsevier

Statistics in Medicine

  • 5th Edition - May 1, 2027
  • Latest edition
  • Authors: Robert H. Riffenburgh, Daniel L. Gillen
  • Language: English

Statistics in Medicine, 5th Edition serves as an essential resource for health care students, professionals and researchers seeking to understand the application of statis… Read more

Description

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, polytomous response regression, classification and regression trees, and the integration of machine learning and AI with statistical methodologies. Each chapter provides practical examples and step-by-step methods, helping readers understand 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.

Key features

  • 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, classification and regression trees, and the interplay between statistics, machine learning, and AI, addressing the evolving landscape of medical statistics and research methodologies

Readership

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

Table of contents

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

Product details

  • Edition: 5
  • Latest edition
  • Published: May 1, 2027
  • Language: English

About the authors

RR

Robert H. Riffenburgh

Robert H. Riffenburgh, PhD, PStat, Fellow American Statistical Association, Fellow Royal Statistical Society, retired in 2021 at age 90 as Professor Emeritus at San Diego State University. He has also been a company CEO, government scientist, oceanometrician, Navy undersea diver, NATO officer in Europe, and medical research planner/analyst. He published 165 scientific articles and edited two journal series. Since retiring, he has published two novels, penned 50 short stories, and won the Odin Writers Award.
Affiliations and expertise
Naval Medical Center, San Diego, California, USA

DG

Daniel L. Gillen

Daniel L. Gillen, PhD., is Chancellor’s Professor of Statistics, Epidemiology and Biostatistics, and Global Health & Biobehavioral Sciences at the University of California, Irvine (UCI). He is also Associate Dean for Experiential Learning in the Donald Bren School of Information and Computer Sciences at UCI. He is a Fellow of the American Association for the Advancement of Science, 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 previously led 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 more than 80 independent safety monitoring boards for multicenter international clinical trials. He has published over 220 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