Robert H. Riffenburgh, Daniel L. Gillen

Statistics in Medicine (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 822
• Erscheinungstermin: 3. Juli 2020
• Englisch
• ISBN-13: 9780128153291
• Artikelnr.: 61688288

Autorenporträt

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.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.

Inhaltsangabe

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