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Statistics in Medicine, Third Edition

Author:
Robert H. Riffenburgh
Publisher: Academic Press
Copyright: 2012
ISBN-13: 978-0-12-384864-2
Pages: 690; hardcover
Price: $44.75

Comment from the Stata technical group

Statistics in Medicine, Third Edition, by Robert H. Riffenburgh, is an excellent book, useful as a reference for researchers in the medical sciences and as a textbook. It focuses largely on understanding statistical concepts rather than on mathematical and theoretical underpinnings. Riffenburgh covers both introductory statistical techniques and advanced methods commonly appearing in medical journals.

Riffenburgh begins with a discussion related to planning studies and writing articles to report results. Following this, he introduces statistics that would typically be covered in an introductory biostatistics course. These include summary statistics, distributions, two-way tables, confidence intervals, and hypothesis tests. In addition, he gives an overview of a variety of more sophisticated statistical techniques such as advanced regression, survival analysis, equivalence testing, Bayesian analysis, and time-series analysis.


Table of contents

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: From Design to Publication
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: What Do I Do with My Data?
2.1. What Is in this Chapter
2.2. Notation (or Symbols)
2.3. Qualification 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 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: Concept and Practice
8.1. Hypothesis in Inference
8.2. Error Probabilities
8.3. Two Policies of Testing
8.4. Organizing Data for Interference
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 x c Contingency Tables
9.6. Tests on 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 Equibalance 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 Estimation 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 Means
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 Recrusive 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
Chpater 22 Multiple and Curvilinear Regression
22.1. Concepts
22.2. Multiple Regression
22.3. Curvilinear Regression
Chpater 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 Data: 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
Tables of Probability Distributions
Reference and Data Sources
Symbol Index
Statistical Subject Index
Medical Suject Index
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