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