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Statistics in Medicine, Fourth Edition |
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Comment from the Stata technical groupStatistics in Medicine, Fourth Edition, by Robert H. Riffenburgh and Daniel L. Gillen, 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. The authors cover both introductory statistical techniques and advanced methods commonly appearing in medical journals. The text begins with a discussion related to planning studies and writing articles to report results. Following this, it 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, the authors give an overview of a variety of more sophisticated statistical techniques such as regression models for binary and count outcomes, survival analysis, equivalence testing, Bayesian analysis, and meta-analysis. |
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Table of contentsView table of contents >> Foreword to the fourth edition
Acknowledgments
How to use this book?
Chapter 1 Planning studies: from design to publication
1.1 Organizing a Study
1.2 Stages of Scientific Investigation 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 in Observational Studies 1.9 Sampling Bias 1.10 Randomizing 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 1.17 Conclusion Appendix to Chapter 1 Chapter 2 Planning analysis: how to reach my scientific objective
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 and Types of Adjustment Variables 2.6 How to Manage Data 2.7 Defining the Scientific Goal: Description, Association Testing, Prediction 2.8 Reporting Statistical Results 2.9 A First-Step Guide to Descriptive Statistics 2.10 An Overview of Association Testing 2.11 A Brief Discussion of Prediction Modeling 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 References 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 Sampling Distribution of a Variable Versus a Statistic 4.7 Statistical Inference 4.8 Distributions Commonly Used in Statistics 4.9 Approximate Distribution of the Mean (Central Limit Theorem) 4.10 Approximate Distribution of a Sample Quantile Chapter 5 Descriptive Statistics
5.1 Purpose of Descriptive Statistics
5.2 Numerical Descriptors, One Variable 5.3 Numerical Descriptors, Two Variables 5.4 Numerical Descriptors, Three Variables 5.5 Graphical Descriptors, One Variable 5.6 Graphical Descriptors, Two Variables 5.7 Graphical Descriptors, Three Variables 5.8 Principles of Informative Descriptive Tables and Figures References 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 References Chapter 7 Hypothesis testing: concept and practice
7.1 Hypotheses in Inference
7.2 Error Probabilities 7.3 Two Policies of Testing 7.4 Distinguishing Between Statistical and Clinical Significance 7.5 Controversies Regarding the Rigid Use and Abuse of p-Values 7.6 Avoiding Multiplicity Bias 7.7 Organizing Data for Inference 7.8 Evolving a Way to Answer Your Data Question Reference Chapter 8 Tolerance, prediction, and confidence intervals
8.1 Overview
8.2 Tolerance Intervals for Patient Measurements 8.3 Concept of a Confidence Interval for a Parameter 8.4 Confidence Interval for a Population Mean, Known Standard Deviation 8.5 Confidence Interval for a Population Mean, Estimated Standard Deviation 8.6 Confidence Interval for a Population Proportion 8.7 Confidence Interval for a Population Median 8.8 Confidence Interval for a Population Variance or Standard Deviation 8.9 Confidence Interval for a Population Correlation Coefficient References 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 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 9.10 Three or More Ranked Samples With Two Outcome Categories: Royston's Ptrend Test References Chapter 10 Risks, odds, and receiver operating characteristic curves
10.1 Association Measures for Categorical Data: Risks and Odds
10.2 Inference for the Risk Ratio: The Log Risk Ratio Test 10.3 Inference for the Odds Ratio: The Log Odds Ratio Test 10.4 Receiver Operation Characteristic Curves 10.5 Comparing Two Receiver Operating Characteristic Curves References Chapter 11 Tests of location with continuous outcomes
11.1 Basics of Location Testing
11.2 Single or Paired Means: One-Sample Normal (z) and t Tests 11.3 Two Means Two-Sample Normal (z) and t Tests 11.4 Three or More Means: One-Factor Analysis of Variance 11.5 Three or More Means in Rank Order: Analysis of Variance Trend Test 11.6 The Basics of Nonparametric Tests 11.7 Single or Paired Sample Distribution(s): The Signed-Rank Test 11.8 Two Independent Sample Distributions: The Rank-Sum Test 11.9 Large Sample-Ranked Outcomes 11.10 Three or More Independent Sample Distributions: The Kruskal—Wallis Test 11.11 Three or More Matched Sample Distributions: The Friedman Test 11.12 Three or More Ranked Independent Samples With Ranked Outcomes: Cusick's Nptrend Test 11.13 Three or More Ranked Matched Samples With Ranked Outcomes: Page's L Test 11.14 Potential Drawbacks to Using Nonparametric Tests Reference Chapter 12 Equivalence testing
12.1 Concepts and Terms
12.2 Basics Underlying Equivalence Testing 12.3 Choosing a Noninferiority or Equivalence Margin 12.4 Methods for Noninferiority Testing 12.5 Methods for Equivalence Testing 12.6 Joint Difference and Equivalence Testing References Chapter 13 Tests on variability and distributions
13.1 Basics of Tests on Variability
13.2 Testing Variability on a Single Sample 13.3 Testing Variability Between Two Samples 13.4 Testing Variability Among Three or More Samples 13.5 Basics on Tests of Distributions 13.6 Test of Normality of a Distribution 13.7 Test of Equality of Two Distributions References Chapter 14 Measuring association and agreement
14.1 What Are Association and Agreement?
14.2 Contingency as Association 14.3 Correlation as Association 14.4 Contingency as Agreement 14.5 Correlation as Agreement 14.6 Agreement Among Ratings: Kappa 14.7 Agreement Among Multiple Rankers 14.8 Reliability 14.9 Intraclass Correlation References Chapter 15 Linear regression and correlation
15.1 Introduction
15.2 Regression Concepts and Assumptions 15.3 Simple Regression 15.4 Assessing Regression: Tests and Confidence Intervals 15.5 Deming Regression 15.6 Types of Regression 15.7 Correlation Concepts and Assumptions 15.8 Correlation Coefficients 15.9 Correlation as Related to Regression 15.10 Assessing Correlation: Tests and Confidence Intervals 15.11 Interpretation of Small-But-Significant Correlations References Chapter 16 Multiple linear and curvilinear regression and multifactor analysis of variance
16.1 Introduction
16.2 Multiple Linear Regression 16.3 Model Diagnosis and Goodness of Fit 16.4 Accounting for Heteroscedasticity 16.5 Curvilinear Regression 16.6 Two-Factor Analysis of Variance 16.7 Analysis of Covariance 16.8 Three-Way and Higher Way Analysis of Variance 16.9 Concepts of Experimental Design References Chapter 17 Logistic regression for binary outcomes
17.1 Introduction
17.2 Extensions of Contingency Table Analyses Simple Logistic Regression 17.3 Multiple Logistic Regression Model Specification and Interpretation 17.4 Inference for Association Parameters 17.5 Model Diagnostics and Goodness-of-Fit References Chapter 18 Poisson regression for count outcomes
18.1 Introduction
18.2 The Poisson Distribtution 18.3 Means Versus Rates 18.4 Inference for the Rate of a Poisson Random Variable 18.5 Comparing Poisson Rates From Two Independent Samples 18.6 The Simple Poisson Regression Model 18.7 Multiple Poisson Regression: Model Specification and Interpretation 18.8 Obtaining Predicted Rates 18.9 Inference for Association Parameters Reference Chapter 19 Analysis of censored time-to-event data
19.1 Survival Concepts
19.2 Censoring 19.3 Survival Estimation: Life Table Estimates and Kaplan—Meier Curves 19.4 Survival Testing: The Log-Rank Test 19.5 Adjusted Comparison of Survival Times: Cox Regression References Chapter 20 Analysis of repeated continuous measurements over time
20.1 Introduction
20.2 Distinguishing Longitudinal Data From Time-Series Data 20.3 Analysis of Longitudinal Data 20.4 Time-Series: References Chapter 21 Sample size estimation
21.1 Issues in Sample Size Considerations
21.2 Is the Sample Size Estimate Adequate 21.3 The Concept of Power Analysis 21.4 Sample Size Methods 21.5 Test on One Mean (Normal Distribution) 21.6 Test on Two Means (Normal Distribution) 21.7 Tests When Distributions Are Nonnormal or Unknown 21.8 Test With No Objective Prior Data 21.9 Confidence Intervals on Means 21.10 Test of One Proportion (One Rate) 21.11 Test of Two Proportions (Two Rates) 21.12 Confidence Intervals on Proportions (On Rates) 21.13 Test on a Correlation Coefficient 21.14 Tests on Ranked Data 21.15 Variance Tests, Analysis of Variance, and Regression 21.16 Equivalence Tests 21.17 Number Needed to Treat or Benefit References Chapter 22 Clinical trials and group sequential testing
22.1 Introduction
22.2 Fundamentals of Clinical Trial Design 22.3 Reducing Bias in Clinical Trials Blinding and Randomization 22.4 Interim Analyses in Clinical Trials: Group Sequential Testing References Chpater 23 Epidemiology
23.1 The Nature of Epidemiology
23.2 Some Key Stages in the History of Epidemiology 23.3 Concept of Disease Transmission 23.4 Descriptive Measures 23.5 Types of Epidemiologic Studes 23.6 Retrospective Study Designs: The Case—Control Study Design 23.7 The Nested Case—Control Study Design 23.8 The Case—Cohort Study Design 23.9 Methods to Analyze Survival and Causal Factors 23.10 A historical note References Chapter 24 Meta-analyses
24.1 Introduction
24.2 Publication Bias in Meta-analyses 24.3 Fixed- and Random-Effects Estimates of the Pooled Effect 24.4 Tests for Heterogeneity of Estimated Effects Across Studies 24.5 Reporting the Results of a Meta-analysis 24.6 Further References References Chapter 25 Bayesian statistics
25.1 What Is Bayesian Statistics
25.2 Bayesian Concepts 25.3 Describing and Testing Means 25.4 On Parameters Other Than Means 25.5 Describing and Testing a Rate (Proportion) 25.6 Conclusion References Chapter 26 Questionnaires and surveys
26.1 Introduction
26.2 Surveys 26.3 Questionnaires Chapter 27 Techniques to Aid Analysis
27.1 Interpreting Results
27.2 Significance in Interpretation 27.3 Post Hoc Confidence and Power 27.4 Multiple Tests and Significance 27.5 Bootstrapping, Resampling, and Simulation 27.6 Bland-Altman Plot: A Diagnostic Tool 27.7 Cost Effectiveness References 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 Markov Chains: Following Multiple States through Time 28.7 Markov Chain Monte Carlo: Evolving Models 28.8 Markov Chain Monte Carlo: Stationary Models 28.9 Further Nonparametric Tests 28.10 Imputation of Missing Data 28.11 Frailty Models in Survival Analysis 28.12 Bonferroni "Correction" 28.13 Logit and Probit 28.14 Adjusting for Outliers 28.15 Curve Fitting to Data 28.16 Sequential Analysis 28.17 Another Test of Normality 28.18 Data Mining 28.19 Data Science and The Relationship Among Statistics, Machine Learning, and Artificial Intelligence References Appendix 1: Answers to exercises: Final
Appendix 2: Databases
Appendix 3: Tables of probability distributions
Appendix 4: Symbol index
Statistical Subject Index
Medical Suject Index
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