Essentials of Biostatistics in Public Health: Edition 3

Jones & Bartlett Learning
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Essentials of Biostatistics in Public Health, Third Edition provides a fundamental and engaging background for students learning to apply and appropriately interpret biostatistics applications in the field of public health. Many examples are drawn directly from the author's remarkable clinical experiences with the renowned Framingham Heart Study, making this text practical, interesting, and accessible for those with little mathematical background. The examples are real, relevant, and manageable in size so that students can easily focus on applications rather than become overwhelmed by computations.
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About the author

Professor and Chair, Department of Biostatistics, Boston University School of Public Health

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Jones & Bartlett Learning
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Feb 17, 2017
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Education / General
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Preface To The Third Edition

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 x 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
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 How High Is High?
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 Rates
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 Confounding By Indication
4.19 Survival Analysis: Life Table Methods
4.20 Cox Proportional Hazards Model
4.21 Selecting Variables For Multivariate Models
4.22 Interactions: Additive and Multiplicative Models

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 Tests 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 Transmission Disequilibrium Tests (TDT)
8.8 Some Additional Concepts and Complexities of Genetic Studies

Chapter 9. Research Ethics And Statistics
9.1 What does statistics have to do with it?
9.2 Protection of Human Research Subjects
9.3 Informed Consent
9.4 Equipoise
9.5 Research Integrity
9.6 Authorship policies
9.7 Data and Safety Monitoring Boards
9.8 Summary

Postscript A Few Parting Comments On The Impact Of Epidemiology On Human Lives
Appendix A. Critical Values Of Chi-square, Z, And T
Appendix B. Fisher'S Exact Test
Appendix C. Kruskal-wallis Nonparametric Test To Compare Several Groups
Appendix D. How To Calculate A Correlation Coefficient
Appendix E. Age-adjustment
Appendix F. Confidence Limits On Odds Ratios
Appendix G. "J" Or "U" Shaped Relationship Between Two Variables
Appendix H. Determining Appropriateness Of Change Scores
Appendix I. Genetic Principles
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