Statistics in Medicine
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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, http://www.elsevierdirect.com/companion.jsp?ISBN=9780123848642
- Medical subject index offers additional search capabilities
Readership
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
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
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