Basic Biostatistics for Geneticists and Epidemiologists: A Practical Approach

ISBN-13: 9780470024898 / Angielski / Twarda / 2008 / 384 str.

Robert C. Elston; William Johnson

Opis

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.

Recenzje

"The book is unusual in having less ambitious goals than the average statistics textbook. The focus is not to teach applications but, as the preface maintains, simply to enable readers to knowledgeably read the new literature, to understand the statistical methods used, and thereby to better keep abreast of new findings in epidemiology and genetics." ( JAMA , September 13, 2010) "This is a well–written and comprehensive review of the basic (and not–so–basic) concepts and techniques in biostatistics. It is understandable to biologists and clinicians, while still providing useful pointers and reminders to statisticians. It is worth a place on the bookshelves of all researchers in genetics, regardless of their statistical expertise." ( Human Genetics, February 2010) "Anyone who wishes to critically read biomedical literature will find the knowledge gained from reading [the text] of great value ." (American Journal of Epidemiology, 2009)

Spis treści

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 Treatment Groups.

Data with Multiple Classifications.

Analysis of Covariance.

Assumptions Associated with the Analysis of Variance.

Summary.

Further Reading.

Problems.

12. SOME SPECIALIZED TECHNIQUES.

Multivariate Analysis.

Discriminant Analysis.

Logistic Regression.

Analysis of Survival Times.

Estimating Survival Curves.

Permutation Tests.

Resampling Methods.

Summary.

References.

Further Reading.

Problems.

13. GUIDES TO A CRITICAL EVALUATION OF PUBLISHED REPORTS.

The Research Hypothesis.

Variables Studied.

The Study Design.

Sample Size.

Completeness of the Data.

Appropriate Descriptive Statistics.

Appropriate Statistical Methods for Inferences.

Logic of the Conclusions.

Meta–analysis.

Summary.

Further Reading.

Problems.

EPILOGUE.

REVIEW PROBLEMS.

ANSWERS.

APPENDIX.

INDEX.

O autorze

Robert C Elston, Professor Genetic and Molecular Epidemiology Track, Department of Epidemiology and Biostatistics, School of Medicine Case Western Reserve University, USA. An internationally prominent genetic epidemiologist with many years teaching experience.

William D Johnson, Medical Center, University of Mississippi, USA. An experienced human geneticist.

Blurb

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.