Primer of Biostatistics: Sixth Edition / Edition 6

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Extremely popular, this student-friendly text presents the practical areas of statistics in terms of their relevance to medicine and the life sciences. Includes many illustrative examples and challenging problems that reinforce the author’s unique and intuitive approach to the subject. The new edition features a new two-color design, examples taken from current biomedical literature, and review questions within each chapter.

" the basic principles of biostatistics and demonstrates their usefulness in biomedical research and clinical decision making...the software now has a more graphical interface & performs over 15 analyses of your data

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Editorial Reviews

Doody's Review Service
Reviewer: Sharon M. Homan, PhD (Kansas Health Institute)
Description: This book reviews basic principles of biostatistics and their use in biomedical and clinical research and decision making. The author organizes the text around hypothesis testing and estimation of treatment effects, and particularly emphasizes areas that the medical literature uses dubious practices (e.g., failure to adjust for multiple comparisons). This sixth edition is a substantial revision with updated examples and a complete redesign of illustrations, both improving the presentation of core statistical principles.
Purpose: It is oriented to students, postdoctoral research fellows, professors, and practitioners in medicine. The purpose is to review basic principles of biostatistics, with emphasis on mastering descriptive statistics, hypothesis testing, and research design. The book achieves these objectives using a straightforward teaching style of posing questions (e.g., How do you analyze rates and proportions?) and systematically demonstrating how to address the question. The content is interesting, practical, and is reinforced by review questions and summaries for each chapter.
Audience: This can be used as a text for medical students, health professionals, and researchers. Used as a required text for the biostatistics portion of the epidemiology and biostatistics course required of medical students, this book is clear, concise, and practical. The author is a professor of medicine at the Cancer Center of the University of California, San Francisco, who is recognized for his contributions to using biostatistics in clinical practice.
Features: The book teaches basic descriptive and inferential methods for one and two samples, introduces power, sample size, confidence intervals, and measurement. The author illustrates how to properly use statistical techniques including t tests, linear regression, analysis of variance, rank tests, Kaplan Meier survival curves, and Gehan tests. The highlights of the book are the chapter summaries, excellent examples, and final chapter, "What do the data really show?" The final chapter demonstrates how the research question, research design, and measurement influence the choice of statistical procedures. This edition greatly expands coverage of important statistical techniques used in the medical literature, but logistic regression is omitted. Logistic regression is used much more often in clinical and epidemiological research than linear regression.
Assessment: This well written book is highly useful for medical students and clinicians, and easy to understand. There are many introductory biostatistics and medical statistics texts, but three key features set this one apart: it assumes no prior knowledge of biostatistics, can be read and absorbed by the novice in about 15 hours, and greatly enhances the reader's ability to use and evaluate the clinical literature.
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Product Details

  • ISBN-13: 9780071435093
  • Publisher: McGraw-Hill Companies, The
  • Publication date: 4/15/2005
  • Edition description: REV
  • Edition number: 6
  • Pages: 500
  • Product dimensions: 7.80 (w) x 5.34 (h) x 0.80 (d)

Meet the Author

Stanton A. Glantz, PhD: Professor of Medicine, also Member, Cardiovascular Research Institute, Institute for Health Policy Studies, and Cancer Center, University of California, San Francisco.

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Table of Contents

1 Biostatistics and clinical practice 1
2 How to summarize data 11
3 How to test for differences between groups 40
4 The special case of two groups : the t test 73
5 How to analyze rates and proportions 126
6 What does "not significant" really mean? 179
7 Confidence intervals 219
8 How to test for trends 253
9 Experiments when each subject receives more than one treatment 321
10 Alternatives to analysis of variance and the t test based on ranks 363
11 How to analyze survival data 413
12 What do the data really show? 444
App Power charts 472
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