Statistics in Medicine

Statistics in Medicine

3rd Edition - July 9, 2012

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  • Author: Robert Riffenburgh
  • eBook ISBN: 9780123848659

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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.

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,
  • Medical subject index offers additional search capabilities


Clinicians (in all areas of medicine, dentistry, and veterinary) who plan to conduct medical research or at least read and understand research results. 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)

Table of Contents

  • Dedication 1
    Dedication 2
    Foreword to the Third Edition
    Foreword to the Second Edition
    Foreword to the First Edition
    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

Product details

  • No. of pages: 744
  • Language: English
  • Copyright: © Academic Press 2012
  • Published: July 9, 2012
  • Imprint: Academic Press
  • eBook ISBN: 9780123848659

About the Author

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, USA

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