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Statistics in Medicine
 
 

Statistics in Medicine, 3rd Edition

 
Statistics in Medicine, 3rd Edition,Robert Riffenburgh,ISBN9780123848642
 
 
 

  

Academic Press

9780123848642

9780123848659

744

235 X 191

A comprehensive yet succinct resource for the non-biostatistician in the statistical design, development, and analysis of medical and biomedical studies

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Key Features

  • User-friendly format includes medical examples, step-by-step methods, and check-yourself exercises appealing to readers with little or no statistical background, across medical and biomedical disciplines
  • Facilitates stand-alone methods rather than a required sequence of reading and references to prior text
  • Covers trial randomization, treatment ethics in medical research, imputation of missing data, evidence-based medical decisions, how to interpret medical articles, noninferiority testing, meta-analysis, screening number needed to treat, and epidemiology
  • Fills the gap left in all other medical statistics books between the reader’s knowledge of how to go about research and the book’s coverage of how to analyze results of that research

New in this Edition:

  • New chapters on planning research, managing data and analysis, Bayesian statistics, measuring association and agreement, and questionnaires and surveys
  • New sections on what tests and descriptive statistics to choose, false discovery rate, interim analysis, bootstrapping, Bland-Altman plots, Markov chain Monte Carlo (MCMC), and Deming regression
  • Expanded coverage on probability, statistical methods and tests relatively new to medical research, ROC curves, experimental design, and survival analysis
  • 35 Databases in Excel format used in the book and can be downloaded and transferred into whatever format is needed along with PowerPoint slides of figures, tables, and graphs from the book included on the companion site, http://www.elsevierdirect.com/companion.jsp?ISBN=9780123848642
  • Medical subject index offers additional search capabilities

Description

Statistics in Medicine, Third Edition makes medical statistics easy to understand by students, practicing physicians, and researchers. The book begins with databases from clinical medicine and uses such data to give multiple worked-out illustrations of every method. The text opens with how to plan studies from conception to publication and what to do with your data, and follows with step-by-step instructions for biostatistical methods from the simplest levels (averages, bar charts) progressively to the more sophisticated methods now being seen in medical articles (multiple regression, noninferiority testing). Examples are given from almost every medical specialty and from dentistry, nursing, pharmacy, and health care management. A preliminary guide is given to tailor sections of the text to various lengths of biostatistical courses.

Readership

Primary: Clinicians (in all areas of medicine, dentistry, and veterinary) who plan to conduct medical research or at least read and understand research results. Secondary: medical students, fellows and biomedical graduate students taking biostatistics courses for non-statisticians; professors of medical statistics and biostatistics (who are themselves medical statisticians and biostatisticians)

Robert 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, U.S.A.

Statistics in Medicine, 3rd Edition

Dedication 1

Dedication 2

Foreword to the Third Edition

Foreword to the Second Edition

Foreword to the First Edition

Acknowledgments

Databases

How to Use This Book

Chapter 1. Planning Studies

1.1 Organizing a Study

1.2 Stages of Scientific Knowledge

1.3 Science Underlying Clinical Decision Making

1.4 Why Do We Need Statistics?

1.5 Concepts in Study Design

1.6 Study Types

1.7 Convergence with Sample Size

1.8 Sampling Schemes

1.9 Sampling Bias

1.10 How to Randomize a Sample

1.11 How to Plan and Conduct a Study

1.12 Mechanisms to Improve your Study Plan

1.13 Reading Medical Articles

1.14 Where Articles May Fall Short

1.15 Writing Medical Articles

1.16 Statistical Ethics in Medical Studies

Appendix to Chapter 1

Chapter 2. Planning Analysis

2.1 What is in this Chapter

2.2 Notation (or Symbols)

2.3 Quantification and Accuracy

2.4 Data Types

2.5 Multivariable Concepts

2.6 How to Manage Data

2.7 A First Step Guide to Descriptive Statistics

2.8 Setting Up a Test Within a Study

2.9 Choosing the Right Test

2.10 A First Step Guide to Tests of Rates or Averages

2.11 A First Step Guide to Tests of Variability

2.12 A First Step Guide to Tests of Distributions

Appendix to Chapter 2

Chapter 3. Probability and Relative Frequency

3.1 Probability Concepts

3.2 Probability and Relative Frequency

3.3 Graphing Relative Frequency

3.4 Continuous Random Variables

3.5 Frequency Distributions for Continuous Variables

3.6 Probability Estimates from Continuous Distributions

3.7 Probability as Area under the Curve

Chapter 4. Distributions

4.1 Characteristics of a Distribution

4.2 Greek Versus Roman Letters

4.3 What is Typical

4.4 The Spread about the Typical

4.5 The Shape

4.6 Statistical Inference

4.7 Distributions Commonly Used in Statistics

4.8 Standard Error of the Mean

4.9 Joint Distributions of Two Variables

Chapter 5. Descriptive Statistics

5.1 Numerical Descriptors, One Variable

5.2 Numerical Descriptors, Two Variables

5.3 Pictorial Descriptors, One Variable

5.4 Pictorial Descriptors, multiple Variables

5.5 Good Graphing Practices

Chapter 6. Finding Probabilities

6.1 Probability and Area Under the Curve

6.2 The Normal Distribution

6.3 The t Distribution

6.4 The Chi-Square Distribution

6.5 The F Distribution

6.6 The Binomial Distribution

6.7 The Poisson Distribution

Chapter 7. Confidence Intervals

7.1 Overview

7.2 Confidence Interval on an Observation from an Individual Patient

7.3 Concept of a Confidence Interval on a Descriptive Statistic

7.4 Confidence Interval on a Mean, Known Standard Deviation

7.5 Confidence Interval on a Mean, Estimated Standard Deviation

7.6 Confidence Interval on a Proportion

7.7 Confidence Interval on a Median

7.8 Confidence Interval on a Variance or Standard Deviation

7.9 Confidence Interval on a Correlation Coefficient

Chapter 8. Hypothesis Testing

8.1 Hypotheses in Inference

8.2 Error Probabilities

8.3 Two Policies of Testing

8.4 Organizing Data for Inference

8.5 Evolving a Way to Answer Your Data Question

Chapter 9. Tests on Categorical Data

9.1 Categorical Data Basics

9.2 Tests on Categorical Data: 2 × 2 Tables

9.3 The Chi-Square Test of Contingency

9.4 Fisher’s Exact Test of Contingency

9.5 Tests on r × c Contingency Tables

9.6 Tests of Proportion

9.7 Tests of Rare Events (Proportions Close to Zero)

9.8 Mcnemar’s test: Matched Pair Test of a 2 × 2 Table

9.9 Cochran’s Q: Matched Pair Test of a 2 × r Table

Chapter 10. Risks, Odds, and ROC Curves

10.1 Categorical Data: Risks and Odds

10.2 Receiver Operating Characteristic Curves

10.3 Comparing Two ROC Curves

10.4 The Log Odds Ratio Test of Association

10.5 Confidence Interval on the Odds Ratio

Chapter 11. Tests on Ranked Data

11.1 Rank Data: Basics

11.2 Single or Paired Sample(s), Ranked Outcomes: The Signed-Rank Test

11.3 Large Sample Single or Paired Ranked Outcomes

11.4 Two Independent Samples, Ranked Outcomes: The Rank-Sum Test

11.5 Two Large Independent samples, Ranked Outcomes

11.6 Multiple Independent Samples, Ranked Outcomes: The Kruskal–Wallis Test

11.7 Multiple Matched Samples, Ranked Outcomes: The Friedman Test

11.8 Ranked Independent Samples, Two Outcomes: Royston’s Ptrend Test

11.9 Ranked Independent Samples, Multiple Categorical or Ranked Outcomes: Cusick’s Nptrend Test

11.10 Ranked Matched Samples, Ranked Outcomes: Page’s L Test

Chapter 12. Tests on Means of Continuous Data

12.1 Basics of Means Testing

12.2 Normal (z) and t Tests for Single or Paired Means

12.3 Two Sample Means Tests

12.4 Testing Three or More Means: One-Factor ANOVA

12.5 ANOVA Trend Test

Chapter 13. Multi-Factor ANOVA and ANCOVA

13.1 Concepts of Experimental Design

13.2 Two-Factor ANOVA

13.3 Repeated Measures ANOVA

13.4 Analysis of Covariance (ANCOVA)

13.5 Three-and-Higher-Factor ANOVA

13.6 More Specialized Designs and Techniques

Chapter 14. Tests on Variability and Distributions

14.1 Basics of Tests on Variability

14.2 Testing Variability on a Single Sample

14.3 Testing Variability Between Two Samples

14.4 Testing Variability among Three or more Samples

14.5 Basics on Tests of Distributions

14.6 Test of Normality of a Distribution

14.7 Test of Equality of Two Distributions

Chapter 15. Managing Results of Analysis

15.1 Interpreting Results

15.2 Significance in Interpretation

15.3 Post Hoc Confidence and Power

15.4 Multiple Tests and Significance

15.5 Interim Analysis

15.6 Bootstrapping: When You Can’t Increase Your Sample Size

15.7 Resampling and Simulation

15.8 Bland–Altman Plots

Chapter 16. Equivalence Testing

16.1 Concepts and Terms

16.2 Basics Underlying Equivalence Testing

16.3 Methods for Non-Inferiority Testing

16.4 Methods for Equivalence Testing

Chapter 17. Bayesian Statistics

17.1 What is Bayesian Statistics

17.2 Bayesian Concepts

17.3 Describing and Testing Means

17.4 On Parameters other than Means

17.5 Describing and Testing a Rate (Proportion)

17.6 Conclusion

Chapter 18. Sample Size Estimation and Meta-Analysis

18.1 Issues in Sample Size Considerations

18.2 Is the Sample Size Estimate Adequate?

18.3 The Concept of Power Analysis

18.4 Sample Size Methods in this Chapter

18.5 Test on One Mean (Normal Distribution)

18.6 Test on Two Means (Normal Distribution)

18.7 Test When Distributions are Non-Normal or Unknown

18.8 Test with No Objective Prior Data

18.9 Confidence Intervals on Means

18.10 Test of One Proportion (One Rate)

18.11 Test of Two Proportions (Two Rates)

18.12 Confidence Intervals on Proportions (On Rates)

18.13 Test on a Correlation Coefficient

18.14 Tests on Ranked Data

18.15 Variance Tests, Anova, and Regression

18.16 Equivalence Tests

18.17 Meta-Analysis

Chapter 19. Modeling Concepts and Methods

19.1 What is a “Model”?

19.2 Straight-Line Models

19.3 Curved Models

19.4 Constants of Fit for any Model

19.5 Multiple-Variable Models

19.6 Building Models: Measures of Effectiveness

19.7 Outcomes Analysis

Chapter 20. Clinical Decisions Based on Models

20.1 Introduction

20.2 Clinical Decision Based on Recursive Partitioning

20.3 Number Needed to Treat or Benefit

20.4 Basics of Matrices

20.5 Markov Chain Modeling

20.6 Simulation and Monte Carlo Sampling

20.7 Markov Chain Monte Carlo: Evolving Models

20.8 Markov Chain Monte Carlo: Stationary Models

20.9 Cost Effectiveness

Chapter 21. Regression and Correlation

21.1 Introduction

21.2 Regression Concepts and Assumptions

21.3 Simple Regression

21.4 Assessing Regression: Tests and Confidence Intervals

21.5 Deming Regression

21.6 Types of Regression

21.7 Correlation Concepts and Assumptions

21.8 Correlation Coefficients

21.9 Correlation as Related to Regression

21.10 Assessing Correlation: Tests and Confidence Intervals

21.11 Interpretation of Small-But-Significant Correlations

Chapter 22. Multiple and Curvilinear Regression

22.1 Concepts

22.2 Multiple Regression

22.3 Curvilinear Regression

Chapter 23. Survival, Logistic Regression, and Cox Regression

23.1 Survival Concepts

23.2 Survival Estimation and Kaplan–Meier Curves

23.3 Survival Testing: The Log Rank Test

23.4 Survival Prediction: Logistic Regression

23.5 Survival Time Prediction: Cox Regression

Chapter 24. Sequential Analysis and Time Series

24.1 Introduction

24.2 Sequential Analysis

24.3 Time-Series: Detecting Patterns

24.4 Time-Series Data: Testing Patterns

Chapter 25. Epidemiology

25.1 The Nature of Epidemiology

25.2 Some Key Stages in the History of Epidemiology

25.3 Concept of Disease Transmission

25.4 Descriptive Measures

25.5 Types of Epidemiologic Studies

25.6 An Informal Approach to Public Health Problems

25.7 The Analysis of Survival and Causal Factors

Chapter 26. Measuring Association and Agreement

26.1 What are Association and Agreement?

26.2 Contingency as Association

26.3 Correlation as Association

26.4 Contingency as Agreement

26.5 Correlation as Agreement

26.6 Agreement Among Ratings: Kappa

26.7 Agreement Among Multiple Rankers

26.8 Reliability

26.9 Intra-Class Correlation

Chapter 27. Questionnaires and Surveys

27.1 Introduction

27.2 Surveys

27.3 Questionnaires

Chapter 28. Methods You Might Meet, But Not Every Day

28.1 Overview

28.2 Analysis of Variance Issues

28.3 Regression Issues

28.4 Rates and Proportions Issues

28.5 Multivariate Methods

28.6 Further Non-Parametric Tests

28.7 Imputation of Missing Data

28.8 Frailty Models in Survival Analysis

28.9 Bonferroni “Correction”

28.10 Logit and Probit

28.11 Adjusting for Outliers

28.12 Curve Fitting to Data

28.13 Another Test of Normality

28.14 Data Mining

Answers to Chapter Exercises

Chapter 1

Chapter 2

Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

Chapter 8

Chapter 9

Chapter 10

Chapter 11

Chapter 12

Chapter 13

Chapter 14

Chapter 15

Chapter 16

Chapter 17

Chapter 18

Chapter 19

Chapter 20

Chapter 21

Chapter 22

Chapter 23

Chapter 24

Chapter 25

Chapter 26

Tables of Probability Distributions

Symbol Index

Statistical Subject Index

Medical Subject Index

Quotes and reviews

""…a highly recommended book that is ideally suited for clinicians who require a strong foundation of statistics...The chapter on modeling concepts and methods and the chapter on clinical decision based on models are both extremely important for those medical professionals and researchers who work with clinical trials…a very good choice for an easily understood, yet comprehensive textbook to accompany a course on the subject, as well as a textbook for individual learning.""--Graefe's Archive for Clinical and Experimental Ophthalmology, April 2, 2013
""...if you want a single volume that covers statistics in medicine, you can stop looking…The book is written in a practical and common-sense manner…""--Journal of Clinical Research Best Practices, March 2013
""…there are many clear and varied examples with just enough equations and images to serve their purpose.nbsp; For those of us who learn by walking through a problem, this book is a joy…Dr. Riffenburgh’s text can be a welcome addition to any collection of statistics books.""--Laboratory Animal Practitioner, 46(3): 2013
""This is an excellent resource and reference for students, teachers, and medical professionals. It is also an excellent tool for medical investigators on how to plan and design medical research and how to interpret medical literature in this evidence-based medicine era.""--Doody.com, June 7, 2013

PRAISE FOR THE THIRD EDITION:

""Statistics in Medicine, Third Edition makes medical statistics easy to understand for students, practicing physicians, and researchers…Examples are given from almost every medical specialty and from dentistry, nursing, pharmacy, and health care management.""--Doody.com, April 24, 2013
""I teach MPH, Preventive Medicine residents, Clinical Science and Population Health Science students. I currently use Statistics in Medicine, 2nd Ed…and now am quite fond of it. Its strength is a pedagogical trick of covering the material first at a high level (30,000 ft) and then in detail….My students like the text.""
--Daniel Freeman, PhD, Professor, University of Texas Medical Branch, Galveston TX.
""It is very difficult to avoid much of the basic mathematics without losing some of the important concepts and foundation to the subject. Many authors that try, fail miserably. Riffenburgh [has] carefully crafted a text that succeeds in this goal. I consider Riffenburgh's book to be a great choice especially for a two quarter or two semester course.""--Michael Chernick, PhD, Director of Biostatistical Services, Lankenau Institute for Medical Research, Arlington VA.
""About 90% of statistical analysis uses about 30% of the statistical methods, says Riffenburgh (Naval Medical Center San Diego, California), and those are the methods he devotes his attention to. In a textbook for a first course in statistics for future clinicians (not future mathematicians) he explains the procedures step-by-step with many clinical examples. Among the methods are confidence intervals, hypothesis testing, categorical data, and epidemiological method. He also discusses managing results of analysis, questionnaires and surveys, survival analysis, and logistic regression. The 15 databases he uses are available online. Earlier editions were published in 1999 and 2006. Academic Press is an imprint of Elsevier.""--Reference and Research Book News, October 2012

PRAISE FOR THE SECOND EDITION:

""...the author presents statistical concepts in fairly simple language and in a format that will make it especially appealing to a clinician...All clinicians will find this book useful, whether they are students, residents, or practitioners...particularly helpful to clinicians who do not have the time or interest in getting comprehensive training in biostatistics, yet need to understand and use these concepts in their professional careers...particularly useful are the illustrations and examples, exercises along with their answers, appendixes, and use of real data. Formulas are explained clearly, in a step-by-step fashion...This is one of the few books that presents hard to understand statistical concepts applied to real-world medical data in a relatively simple manner. The second edition expands on the first and includes more advanced topics as well as current concepts.""--DOODY Enterprises, Inc.
""This book is excellent value for the clinician who wants to evaluate the research (s)he reads and for those who carry out research.""--Margaret Moss, UK, for JOURNAL OF NUTRITIONAL amp; ENVIRONMENTAL MEDICINE

FROM THE FOREWORD OF THE SECOND EDITION:
""I congratulate Dr. Riffenburgh on his career as a medical statistician and for this useful text/reference book, which I commend to all who teach statistics to students in the health-related fields and to all who are engaged in or are consumers of health-related research.""--W.M.(Mike) O'Fallon, Professor Emeritus and former Head of Biostatistics and Chair of the Dept. of Health Science Research, Mayo Clinic

PRAISE FOR THE FIRST EDITION:
""This user-friendly text is laid out in a practical fashion that includes numerous examples for applying complex tests. This book is complete enough for even the hard core bioresearcher.""--Richard A. Hill, University of California-Irvine
""Statistics can be overwhelming for the health professional and Dr. Riffenburgh gives them the tools to make preparing proposals using statistical applications easy. I wholeheartedly recommend this book to any professional, novice or seasoned, involved in the research process.""--Capt. Peggy McNulty, U.S. Naval Health Clinic, Hawaii

 
 
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