Since the publication of the first edition, BIOSTATISTICS AND EPIDEMIOLOGY has continued to garner loyal readers from across speciality areas in the biomedical community. BIOSTATISTICS AND EPIDEMIOLOGY provides clear, concise explanations of the underlying principles of statistics and epidemiology, as well as practical guidelines of "how to do it" and "how to interpret it."
Through its several editions, BIOSTATISTICS AND EPIDEMIOLOGY has continued to adapt to evolving areas of research in epidemiology and statistics, while maintaining the original objective of being non-threatening, understandable and accessible to those with limited or no background in mathematics. Two new areas are covered in the third edition: genetic epidemiology and research ethics.
With the sequencing of the human genome, there has been a flowering of research into the genetic basis of health and disease, and especially the interactions between genes and environmental exposures. The medical literature in genetic epidemiology is vastly expanding and some knowledge of the epidemiological designs and an acquaintance with the statistical methods used in such research is necessary in order to be able to appreciate new findings. Thus this edition includes a new chapter on genetic epidemiology as well as an Appendix describing the basics necessary for an understanding of genetic research. Such material is not usually found in first level epidemiology or statistics books, but it is presented here in a basic, and hopefully easily comprehensible way, for those unfamiliar with the field. The second new chapter is on research ethics, also not usually covered in basic textbooks, but critically important in all human research . New material has also been added to several existing chapters.
I hope this book will be useful to diverse groups of people in the health field, as well as to those in related areas. The material is intended for (1) physicians doing clinical research as well as for those doing basic research; (2) for students-medical, college, and graduate; (3) for research staff in various capacities; and (4) for anyone interested in the logic and methodology of biostatistics and epidemiology. The principles and methods described here are applicable to various substantive areas, including medicine, public health, psychology, and education. (from the Preface)
Whether used as a brief textbook in a classroom or as a refresher for more advanced readers, BIOSTATISTICS AND EPIDEMIOLOGY has become a well-thumbed and battered resource in many a classroom, department library and lab. With the explosion of biomedical research and publishing in the past few years, understanding the statistical validity of the results of both basic research and clinical trials has never been more important. This new third edition of BIOSTATISTICS AND EPIDEMIOLOGY serves as the roadmap to understanding these complex and important topics.

Preface To The Third Edition
Acknowledgments
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
Summary:
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
References
Suggested Readings
Index