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Statistics in Medicine, 2nd Edition
Comment from the Stata technical groupStatistics in Medicine is an excellent book for the health professional. It focuses on understanding statistical concepts and not on the mathematical and theoretical underpinnings. The author covers a broad range of statistical techniques with adequate depth and with an appreciation of the problems faced by health professionals. The text consists of two parts. The first resembles a textbook for an introductory course in biostatistics for the medical sciences. Basic concepts, such as summary statistics, confidence intervals, tests, and two-way tables, are covered in a mostly traditional fashion. The second part of the text, however, is meant to serve as a reference for researchers wanting to learn the basic ideas behind more sophisticated techniques, for example, those you would find being used in medical journals or government reports. As such, many topics are covered, such as advanced regressions, survival analysis, equivalence, and experimental design, but no one topic is covered very deeply. Table of contentsForeword to the Second Edition
Foreword to the First Edition
Acknowledgments
Databases
Part I A Study Course of Fundamentals
1 Data, Notation, and Some Basic Terms
1.1. About This Book
1.2. Stages of Scientific Knowledge 1.3. Quantification and Accuracy 1.4. Data Types 1.5. Notation (or Symbols) 1.6. Samples, Populations, and Randomness 2 Distributions
2.1. Frequency Distributions
2.2. Relative Frequencies and Probabilities 2.3. Characteristics of a Distribution 2.4. What Is Typical? 2.5. The Spread about the Typical 2.6. The Shape 2.7. Statistical Inference 2.8. Distributions Commonly Used in Statistics 2.9. Standard Error of the Mean 2.10. Joint Distributions of Two Variables Chapter Exercises 3 Summary Statistics
3.1. Numerical Summaries, One Variable
3.2. Numerical Summaries, Two Variables 3.3. Pictorial Summaries, One Variable 3.4. Pictorial Summaries, Two Variables 3.5. Good Graphing Practices 4 Confidence Intervals and Probability
4.1. Overview
4.2. The Normal Distribution 4.3. Confidence Interval on an Observation from an Individual Patient 4.4. Concept of a Confidence Interval on a Descriptive Statistic 4.5. Confidence Interval on a Mean, Known Standard Deviation 4.6. The t Distribution 4.7. Confidence Interval on a Mean, Estimated Standard Deviation 4.8. The Chi-Square Distribution 4.9. Confidence Interval on a Variance or Standard Deviation 4.10. Other Frequently Seen Confidence Intervals and Probabilities 5 Hypothesis Testing: Concept and Practice
5.1. Hypotheses in Inference
5.2. Error Probabilities 5.3. Two Policies of Testing 5.4. Organizing Data for Inference 5.5. Evolving a Way to Answer Your Data Question 6 Statistical Testing, Risks, and Odds in Medical Decisions
6.1. Overview
6.2. Categorical Data: Basics
6.3. Categorical Data: Tests on 2 x 2 Tables 6.4. Categorical Data: Risks and Odds 6.5. Rank Data: Basics 6.6. Rand Data: The Rank-Sum Test to Compare Two Samples 6.7. Continuous Data: Basics of Means 6.8. Continuous Data: Normal (z) and t Tests to Compare Two Sample Means 6.9. Other Tests of Hypotheses 7 Sample Size Required for a Study
7.1. Overview
7.2. Is the Estimate of Minimum Required Sample Size Adequate? 7.3. Sample Size in Means Testing 7.4. Minimum Sample Size Estimation for a Test of Two Means 7.5. Other Situations in Which Minimum Sample Size Estimation Is Used 8 Statistical Prediction
8.1. What Is a "Model"?
8.2. Straight Line Models 8.3. What Is "Regression" (and Its Relation to Correlation)? 8.4. Assessing and Predicting Relationships by Regression 8.5. Other Questions That Can Be Answered by Regression 8.6. Clinical Decisions and Outcomes Analysis 9 Epidemiology
9.1. The Nature of Epidemiology
9.2. Some Key Stages in the History of Epidemiology 9.3. Concept of Disease Transmission 9.4. Descriptive Measures 9.5. Types of Epidemiologic Studies 9.6. An Informal Approach to Public Health Problems 9.7. Analysis of Survival and Causal Factors 10 Reading Medical Articles
10.1. Assessing Medical Information from an Article
10.2. Keep in Mind How a Study Is Constructed 10.3. Study Types 10.4. Sampling Bias 10.5. Statistical Aspects Where Articles May Fall Short 10.6. Evolving Terms: Meta-analysis, Multivariate Analysis, and Others 10.7. Selection of Statistical Tests to Use in a Study Answers to Chapter Exercises, Part I
Part II A Reference Guide
11 Using the Reference Guide
11.1. How to Use This Guide
11.2. Basic Concepts Needed to Use This Guide 12 Planning Medical Studies
12.1. The Science Underlying Clinical Decision Making
12.2. The Objective of Statistics 12.3. Concepts in Study Design 12.4. Sampling Schemes 12.5. How to Randomize a Sample 12.6. How to Plan and Conduct a Study 12.7. Mechanisms to Improve Your Study Plan 12.8. How to Manage Data 12.9. Setting Up a Test Within a Study 12.10. Choosing the Right Test 12.11. Statistical Ethics in Medical Studies 13 Finding Probabilities of Error
13.1. Introduction
13.2. The Normal Distribution 13.3. The t Distribution 13.4. The Chi-Square Distribution 13.5. The F Distribution 13.6. The Binomial Distribution 13.7. The Poisson Distribution 14 Confidence Intervals
14.1. Overview
14.2. Confidence Interval on a Mean, Known Standard Deviation 14.3. Confidence Interval on a Mean, Estimated Standard Deviation 14.4. Confidence Interval on a Variance or Standard Deviation 14.5. Confidence Interval on a Proportion 14.6. Confidence Interval on a Correlation Coefficient 15 Tests on Categorical Data
15.1. Categorical Data Summary
15.2. 2 x 2 Tables: Contingency Tests 15.3. r x c Tables: Contingency Tests 15.4. Risks and Odds in Medical Decisions 15.5. 2 x 2 Tables: Tests of Association 15.6. Tests of a Proportion 15.7. Tests of a Small Proportion (Proportion Close to 0) 15.8. Matched Pair Sample (McNemar's Test) 16 Common Tests on Ranked Data
16.1. Basics of Ranks
16.2. Single or Paired Small Samples: The Signed-Rank Test 16.3. Two Small Samples: The Rank-Sum Test 16.4. Three or More Independent Samples: The Kruskal–Wallis Test 16.5. Three or More Matched Samples: The Friedman Test 16.6. Single Large Samples: Normal Approximation to Signed-Rank Test 16.7. Two Large Samples: Normal Approximation to Rank-Sum Test 17 Tests on Means of Continuous Data
17.1. Summary of Means Testing
17.2. Normal (z) and t Tests for Single or Paired Means 17.3. Post Hoc Confidence and Power 17.4. Normal (z) and t Tests for Two Means 17.5. Three or More Means: One-Way Analysis of Variance 18 Multifactor Tests on Means of Continuous Data
18.1. Concepts of Experimental Design
18.2. Two-Factor Analysis of Variance 18.3. Repeated-Measures Analysis of Variance 18.4. Analysis of Covariance 18.5. Three- and Higher-Factor Analysis of Variance 18.6. More Specialized Designs and Techniques 19 Tests on Variances of Continuous Data
19.1. Basics of Tests on Variability
19.2. Single Samples 19.3. Two Samples 19.4. Three or More Samples 20 Tests on the Distribution Shape of Continuous Data
20.1. Objectives of Tests on Distributions
20.2. Test of Normality of a Distribution 20.3. Test of Equality of Two Distributions 21 Equivalence Testing
21.1. Concepts and Terms
21.2. Basics Underlying Equivalence Testing 21.3. methods for Nonsuperiority Testing 21.4. Methods for Equivalence Testing 22 Sample Size Required in a Study
22.1. Overview
22.2. Relation of Sample Size Calculated to Sample Size Needed 22.3. Sample Size for Tests on Means 22.4. Sample Size for Confidence Intervals on Means 22.5. Sample Size for Tests on Rates (Proportions) 22.6. Sample Size for a Confidence Interval on a Rate (Proportion) 22.7. Sample Size for Significance of a Correlation Coefficient 22.8. Sample Size for Tests on Ranked Data 22.9. Sample Size for Tests on Variances, Analysis of Variance, and Regression 23 Modeling and Clinical Decisions
23.1. Overview of Modeling
23.2. Straight-Line Models 23.3. Curved Models 23.4. Constants of Fit for Any Model 23.5. Multiple-Variable Models 23.6. Clinical Decision Based on Recursive Partitioning 23.7. Screening and Number Needed to Treat or to Benefit 23.8. Clinical Decision Based on Measures of Effectiveness: Outcomes Analysis 24 Regression and Correlation Methods
24.1. Regression Concepts and Assumptions
24.2. Correlation Concepts and Assumptions 24.3. Simple Regression 24.4. Correlation Coefficients 24.5. Tests and Confidence Intervals on Regression Parameters 24.6. Tests and Confidence Intervals on Correlation Coefficients 24.7. Curved Regression 24.8. Multiple Regression 24.9. Types of Regression 24.10. Logistic Regression 25 Survival and Time-Series Analysis
25.1. Time-Dependent Data
25.2. Survival Curves: Estimation 25.3. Survival Curves: Testing 25.4. Sequential Analysis 25.5. Time-Series Data: Detecting Patterns 25.6. Time-Series Data: Testing 26 Methods You Might Meet, But Not Every Day
26.1. Overview
26.2. Analysis of Variance Issues 26.3. Regression Issues 26.4. Multivariate Issues 26.5. Nonparametric Tests 26.6. Imputation of Missing Data 26.7. Resampling Methods 26.8. Agreement Measures and Correlation 26.9. Bonferroni "Correction" 26.10. Logit and Probit 26.11. Adjusting for Outliers 26.12. Curve Fitting to Data 26.13. Tests of Normality Chapter Summaries
References and Data Sources
Tables of Probability Distributions
I. Normal Distribution
II. t Distribution III. Chi-Square Distribution, Right Tail IV. Chi-Square Distribution, Left Tail V. F Distribution VI. Binomial Distribution VII. Poisson Distribution VIII. Signed-Rank Probabilities IX. Rank-Sum U Probabilities Symbol Index
Subject Index
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