Basic Biostatistics for Geneticists and Epidemiologists: A Practical Approach Robert C. Elston, Department of Epidemiology and Biostatistics, Case Western Reserve University, USA. William D. Johnson, Pennington Biomedical Research Center, Louisiana State University, USA. Anyone who attempts to read genetics or epidemiology research literature needs to understand the essentials of biostatistics. This book, a revised new edition of the successful Essentials of Biostatistics has been written to provide such an understanding to those who have little or no statistical background and who need to keep abreast of new findings in this fast moving field. Unlike many other elementary books on biostatistics, the main focus of this book is to explain basic concepts needed to understand statistical procedures. This Book: * Surveys basic statistical methods used in the genetics and epidemiology literature, including maximum likelihood and least squares. * Introduces methods, such as permutation testing and bootstrapping, that are becoming more widely used in both genetic and epidemiological research. * Is illustrated throughout with simple examples to clarify the statistical methodology. * Explains Bayes'' theorem pictorially. * Features exercises, with answers to alternate questions, enabling use as a course text. Written at an elementary mathematical level so that readers with high school mathematics will find the content accessible. Graduate students studying genetic epidemiology, researchers and practitioners from genetics, epidemiology, biology, medical research and statistics will find this an invaluable introduction to statistics.

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PREFACE 1. INTRODUCTION: THE ROLE AND RELEVANCE OF STATISTICS, GENETICS AND EPIDEMIOLOGY IN MEDICINE. Why Statistics? What Exactly Is (Are) Statistics? Reasons for Understanding Statistics What Exactly is Genetics? What Exactly is Epidemiology? How Can a Statistician Help Geneticists and Epidemiologists? Disease Prevention versus Disease Therapy. A Few Examples: Genetics, Epidemiology and Statistical Inference. Summary. References. 2. POPULATIONS, SAMPLES, AND STUDY DESIGN. The Study of Cause and Effect. Populations, Target Populations, and Study Units. Probability Samples and Randomization. Observational Studies. Family Studies. Experimental Studies. Quasi-Experimental Studies. Summary. Further Reading. Problems. 3. DESCRIPTIVE STATISTICS. Why Do We Need Descriptive Statistics? Scales of Measurement. Tables. Graphs. Proportions and Rates. Relative Measures of Disease Frequency. Sensitivity, Specificity, and Predictive Values. Measures of Central Tendency. Measures of Spread or Variabillty. Measures of Shape. Summary. Further Reading. Problems. 4. THE LAWS OF PROBABILITY. Definition of Probability. The Probability of Either of Two Events: A or B. The Joint Probability of Two Events: A and B. Examples of Independence, Nonindependence, and Genetic Counseling. Bayes' Theorem. Likelihood Ratio. Summary. Further Reading. Problems. 5. RANDOM VARIABLES AND DISTRIBUTIONS. Variability and Random Variables. Binomial Distribution. A Note about Symbols. Poisson Distribution. Uniform Distribution. Normal Distribution. Cumulative Distribution Functions. The Standard Normal (Gaussian) Distribution. Summary. Further Reading. Problems. 6. ESTIMATES AND CONFIDENCE LIMITS. Estimates and Estimators. Notation for Population Parameters, Sample Estimates, and Sample Estimators. Properties of Estimators. Maximum Likelihood. Estimating Intervals. Distribution of the Sample Mean. Confidence Limits. Summary. Problems. 7. SIGNIFICANCE TESTS AND TESTS OF HYPOTHESES. Principle of Significance Testing. Principle of Hypothesis Testing. Testing a Population Mean. One-Sided versus Two-Sided Tests. Testing a Proportion. Testing the Equality of Two Variances. Testing the Equality of Two Means. Testing the Equality of Two Medians. Validity and Power. Summary. Further Reading. Problems. 8. LIKELIHOOD RATIOS, BAYESIAN METHODS AND MULTIPLE HYPOTHESES. Likelihood Ratios. Bayesian Methods. Bayes Factors. Bayesian Estimates and Credible Intervals. The Multiple Testing Problem. Summary. Problems. 9. THE MANY USES OF CHI-SQUARE. The Chi-Square Distribution. Goodness-of-Fit Tests. Contingency Tables. Inference About the Variance. Combining p-Values. Likelihood Ratio Tests. Summary. Further Reading . Problems. 10. CORRELATION AND REGRESSION. Simple Linear Regression. The Straight-Line Relationship When There is Inherent Variability. Correlation. Spearman's Rank Correlation. Multiple Regression. Multiple Correlation and Partial Correlation. Regression toward the Mean. Summary. Further Reading. Problems. 11. ANALYSIS OF VARIANCE AND LINEAR MODELS. Multiple Treatment Groups. Completely Randomized Design with a Single Classification of Treatm ...