Free download Biostatistics and Epidemiology A Primer for Health and Biomedical Professionals By Sylvia Wassertheil Smoller

4^th edition

Authors of: Biostatistics and Epidemiology A Primer for Health and Biomedical Professionals By Sylvia Wassertheil Smoller

Sylvia Wassertheil Smoller

Jordan Smoller

Table of Contents in Biostatistics and Epidemiology A Primer for Health and Biomedical Professionals By Sylvia Wassertheil Smoller

PREFACE TO THE FOURTH EDITION  

ACKNOWLEDGEMENTS  

AUTHOR BIOGRAPHY  

CHAPTER 1. THE SCIENTIFIC METHOD  

1.1 The Logic of Scientific Reasoning  

1.2 Variability of Phenomena Requires Statistical Analysis  

1.3 Inductive Inference: Statistics as the Technology

of the Scientific Method  

1.4 Design of Studies  

1.5 How to Quantify Variables  

1.6 The Null Hypothesis  

1.7 Why Do We Test the Null Hypothesis?  

1.8 Types of Errors  

1.9 Significance Level and Types of Error  

1.10 Consequences of Type I and Type II Errors  

CHAPTER 2. A LITTLE BIT OF PROBABILITY  

2.1 What Is Probability?  

2.2 Combining Probabilities  

2.3 Conditional Probability  

2.4 Bayesian Probability  

2.5 Odds and Probability  

2.6 Likelihood Ratio  

2.7 Summary of Probability  

CHAPTER 3. MOSTLY ABOUT STATISTICS  

3.1 Chi-Square for 2  2 Tables  

3.2 McNemar Test  

3.3 Kappa  

3.4 Description of a Population: Use of the Standard

Deviation   

3.5 Meaning of the Standard Deviation: The Normal

Distribution  

3.6 The Difference Between Standard Deviation

and Standard Error  

3.7 Standard Error of the Difference Between Two Means  

3.8 Z Scores and the Standardized Normal Distribution  

3.9 The t Statistic  

3.10 Sample Values and Population Values Revisited  

3.11 A Question of Confidence  

3.12 Confidence Limits and Confidence Intervals  

3.13 Degrees of Freedom  

3.14 Confidence Intervals for Proportions  

3.15 Confidence Intervals Around the Difference

Between Two Means  

3.16 Comparisons Between Two Groups  

3.17 Z-Test for Comparing Two Proportions  

3.18 t-Test for the Difference Between Means

of Two Independent Groups: Principles  

3.19 How to Do a t-Test: An Example  

3.20 Matched Pair t-Test  

3.21 When Not to Do a Lot of t-Tests: The Problem

of Multiple Tests of Significance  

3.22 Analysis of Variance: Comparison Among

Several Groups  

3.23 Principles Underlying Analysis of Variance  

3.24 Bonferroni Procedure: An Approach to Making

Multiple Comparisons  

3.25 Analysis of Variance When There Are Two Independent

Variables: The Two-Factor ANOVA  

3.26 Interaction Between Two Independent Variables  

3.27 Example of a Two-Way ANOVA  

3.28 Kruskal–Wallis Test to Compare Several Groups  

3.29 Association and Causation: The Correlation Coefficient  

3.30 Some Points to Remember About Correlation  

3.31 Causal Pathways   

3.32 Regression  

3.33 The Connection Between Linear Regression

and the Correlation Coefficient  

3.34 Multiple Linear Regression  

3.35 Summary So Far  

CHAPTER 4. MOSTLY ABOUT EPIDEMIOLOGY  

4.1 The Uses of Epidemiology  

4.2 Some Epidemiologic Concepts: Mortality Rates  

4.3 Age-Adjusted Rates  

4.4 Incidence and Prevalence  

4.5 Standardized Mortality Ratio  

4.6 Person-Years of Observation  

4.7 Dependent and Independent Variables  

4.8 Types of Studies  

4.9 Cross-Sectional Versus Longitudinal Looks at Data  

4.10 Measures of Relative Risk: Inferences from Prospective

Studies (the Framingham Study)  

4.11 Calculation of Relative Risk from Prospective Studies  

4.12 Odds Ratio: Estimate of Relative Risk from

Case–Control Studies  

4.13 Attributable Risk  

4.14 Response Bias  

4.15 Confounding Variables  

4.16 Matching  

4.17 Multiple Logistic Regression  

4.18 Survival Analysis: Life Table Methods  

4.19 Cox Proportional Hazards Model  

4.20 Overlapping Confidence Intervals and Statistical

Significance  

4.21 Confounding by Indication  

4.22 Propensity Analysis  

4.23 Selecting Variables for Multivariate Models  

4.24 Interactions: Additive and Multiplicative Models  

4.25 Nonlinear Relationships: J Shape or U Shape  

CHAPTER 5. MOSTLY ABOUT SCREENING  

5.1 Sensitivity, Specificity, and Related Concepts  

5.2 Cutoff Point and Its Effects on Sensitivity and Specificity  

CHAPTER 6. MOSTLY ABOUT CLINICAL TRIALS  

6.1 Features of Randomized Clinical Trials  

6.2 Purposes of Randomization  

6.3 How to Perform Randomized Assignment  

6.4 Two-Tailed Test Versus One-Tailed Test  

6.5 Clinical Trial as “Gold Standard”  

6.6 Regression Toward the Mean  

6.7 Intention-to-Treat Analysis  

6.8 How Large Should the Clinical Trial Be?  

6.9 What Is Involved in Sample Size Calculation?  

6.10 How to Calculate Sample Size for the Difference Between

Two Proportions  

6.11 How to Calculate Sample Size for Testing the Difference

Between Two Means  

CHAPTER 7. MOSTLY ABOUT QUALITY OF LIFE  

7.1 Scale Construction  

7.2 Reliability  

7.3 Validity  

7.4 Responsiveness  

7.5 Some Potential Pitfalls  

CHAPTER 8. MOSTLY ABOUT GENETIC EPIDEMIOLOGY  

8.1 A New Scientific Era  

8.2 Overview of Genetic Epidemiology  

8.3 Twin Studies  

8.4 Linkage and Association Studies  

8.5 LOD Score: Linkage Statistic  

8.6 Association Studies  

8.7 Candidate Gene Association Studies  

8.8 Population Stratification or Population Structure  

8.9 Family-Based Association and the Transmission

Disequilibrium Test (TDT)  

8.10 Genomewide Association Studies: GWAS  

8.11 GWAS Quality Control and Hardy–Weinberg Equilibrium  

8.12 Quantile by Quantile Plots or Q-Q Plots  

8.13 Problem of False Positives  

8.14 Problem of False Negatives  

8.15 Manhattan Plots  

8.16 Polygene Scores  

8.17 Rare Variants and Genome Sequencing  

8.18 Analysis of Rare Variant Studies  

8.19 What’s in a Name? SNPs and Genes  

CHAPTER 9. RISK PREDICTION AND RISK

CLASSIFICATION  

9.1 Risk Prediction  

9.2 Additive Value of a Biomarker: Calculation

of Predicted Risk  

9.3 The Net Reclassification Improvement Index  

9.4 The Category-Less NRI  

9.5 Integrated Discrimination Improvement (IDI)  

9.6 C-Statistic  

9.7 Caveats  

9.8 Summary  

CHAPTER 10. RESEARCH ETHICS AND STATISTICS  

10.1 What Does Statistics Have to Do with It?  

10.2 Protection of Human Research Subjects  

10.3 Informed Consent  

10.4 Equipoise  

10.5 Research Integrity  

10.6 Authorship Policies  

10.7 Data and Safety Monitoring Boards  

10.8 Summary   

Postscript A FEW PARTING COMMENTS ON THE

IMPACT OF EPIDEMIOLOGY ON HUMAN

LIVES  

Appendix 1. CRITICAL VALUES OF CHI-SQUARE,

Z, AND t  

Appendix 2. FISHER’S EXACT TEST  

Appendix 3. KRUSKAL–WALLIS NONPARAMETRIC

TEST TO COMPARE SEVERAL GROUPS  

Appendix 4. HOW TO CALCULATE A CORRELATION

COEFFICIENT  

Appendix 5. AGE-ADJUSTMENT  

Appendix 6. DETERMINING APPROPRIATENESS

OF CHANGE SCORES  

REFERENCES  

SUGGESTED READINGS  

INDEX

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