Epidemiology: Study Design and Data Analysis, Second Edition

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ISBN 9781584884156
Cat# C415X
 

Features

  • Requires little statistical background - ideal for students in epidemiology as well as biostatistics
  • Uses real case studies to illustrate principles more clearly
  • Includes a wealth of new material on topics such as systematic evaluation, linear mixed models, and case-cohort studies
  • Contains a new chapter on meta-analysis
  • Includes a wealth of supporting material on a Web site
  • Summary

    Building an up-to-date understanding of the methodologies that can be used to shape public health policies, Epidemiology: Study Design and Data Analysis, Second Edition encompasses the study of epidemiology from the observation of associations between risk factors and disease to the use of practical, data-supported analyses. It presents study designs commonly used for a wide range of purposes, and covers the spectrum of statistical principles and analytical tools used in epidemiological research, such as techniques used in report writing, descriptive analyses, statistical models and synthesis of evidence.

    New Material in This Edition Includes:

  • Systematic evaluation
  • Meta-analysis
  • Regression dilution
  • Case-cohort studies
  • Case-crossover studies
  • Pooled logistic regression
  • Companion Web site containing data sets for examples and exercises, SAS and Stata code for examples, a sample size calculator, and a SAS floating absolute risk macro

    The second edition of a popular textbook, this book emphasizes quantitative and design aspects of epidemiological research. The author favors the use of basic mathematics and practical methods over complicated mathematical proofs, making this an ideal textbook that is comprehensive yet accessible to graduate students in epidemiology, statistics, public health studies, and/or medical research.
  • Table of Contents

    PREFACE

    FUNDAMENTAL ISSUES
    What is Epidemiology?
    Case Studies: The Work of Doll and Hill
    Populations and Samples
    Measuring Disease
    Measuring the Risk Factor
    Causality
    Studies Using Routine Data
    Study Design
    Data Analysis
    Exercises

    BASIC ANALYTICAL PROCEDURES
    Introduction
    Case Study
    Types of Variables
    Tables and Charts
    Inferential Techniques for Categorical Variables
    Descriptive Techniques for Quantitative Variables
    Inferences about Means
    Inferential Techniques for Non-Normal Data
    Measuring Agreement
    Assessing Diagnostic Tests
    Exercises

    ASSESSING RISK FACTORS
    Risk and Relative Risk
    Odds and Odds Ratio
    Relative Risk or Odds Ratio?
    Prevalence Studies
    Testing Association
    Risk Factors Measured at Several Levels
    Attributable Risk
    Rate and Relative Rate
    Measures of Difference
    Exercises

    CONFOUNDING AND INTERACTION
    Introduction
    The Concept of Confounding
    Identification of Confounders
    Assessing Confounding
    Standardization
    Mantel-Haenszel Methods
    The Concept of Interaction
    Testing for Interaction
    Dealing with Interaction
    Exercises

    COHORT STUDIES
    Design Considerations
    Analytical Considerations
    Cohort Life Tables
    Kaplan-Meier Estimation
    Comparison of Two Sets of Survival Probabilities
    The Person-Years Method
    Period-Cohort Analysis
    Exercises

    CASE-CONTROL STUDIES
    Basic Design Concepts
    Basic Methods of Analysis
    Selection of Cases
    Selection of Controls
    Matching
    The Analysis of Matched Studies
    Nested Case-Control Studies
    Case-Cohort Studies
    Case-Crossover Studies
    Exercises

    INTERVENTION STUDIES
    Introduction
    Ethical Considerations
    Avoidance of Bias
    Parallel Group Studies
    Cross-Over Studies
    Sequential Studies
    Allocation to Treatment Group
    Exercises

    SAMPLE SIZE DETERMINATION
    Introduction
    Power
    Testing a Mean Value
    Testing a Difference Between Means
    Testing a Proportion
    Testing a Relative Risk
    Case-Control Studies
    Complex Sampling Designs
    Concluding Remarks
    Exercises

    MODELLING QUANTITATIVE OUTCOME VARIABLES
    Statistical Models
    One Categorical Explanatory Variable
    One Quantitative Explanatory Variable
    Two Categorical Explanatory Variables
    Model Building
    General Linear Models
    Several Explanatory Variables
    Model Checking
    Confounding
    Longitudinal Data
    Non-Normal Alternatives
    Exercises

    MODELLING BINARY OUTCOME DATA
    Introduction
    Problems with Standard Regression Models
    Logistic Regression
    Interpretation of Logistic Regression Coefficients
    Generic Data
    Multiple Logistic Regression Models
    Tests of Hypotheses
    Confounding
    Interaction
    Model Checking
    Regression Dilution
    Case-Control Studies
    Outcomes with Several Ordered Levels
    Longitudinal Data
    Complex Sampling Designs
    Exercises

    MODELLING FOLLOW-UP DATA
    Introduction
    Basic Functions of Survival Time
    Estimating the Hazard Function
    Probability Models
    Proportional Hazards Regression Models
    The Cox Proportional Hazards Model
    The Weibull Proportional Hazards Model
    Model Checking
    Poisson Regression
    Pooled Logistic Regression
    Exercises

    META-ANALYSIS
    Reviewing Evidence
    Systematic Review
    A General Approach to Pooling
    Investigating Heterogeneity
    Pooling Tabular Data
    Individual Participant Data
    Dealing with Aspects of Study Quality
    Publication Bias
    Is Meta-Analysis a Valid Tool in Epidemiology?
    Exercises

    APPENDIX A: MATERIALS AVAILABLE FROM THE WEBSITE
    APPENDIX B: STATISTICAL TABLES
    APPENDIX C: EXAMPLE DATA SETS
    SOLUTIONS TO EXERCISES
    REFERENCES
    INDEX

    Editorial Reviews

    "As a text in quantitative Epidemiology, this book also works nicely as a text in Biostatistics…The presentation style is relaxed, the examples are helpful, and the level of technical difficulty makes the material approachable without oversimplification…It is sufficiently broad and deep in coverage to compete with standard texts in the field and has the added bonus of emphasizing study design. Methods and issues related to designs commonly used in a wide variety of health sciences are included…"
    -Ken Hess, Department of Biomathematics and Biostatistics, Anderson Cancer Center

    "The second edition of this epidemiology text is strengthened to cater to the two audiences the author has in mind: applied statisticians wishing to learn how their statistical expertise can be used in the epidemiology field and statistic-curious researchers who want to understand how statistical techniques can be used to solve epidemiological problems. …The result is a book that will invariably appeal to the intended audience, one with practical applications of techniques and interpretations of results in an epidemiological context. …The book is most certainly an ambitious attempt at covering a broad array of the most important epidemiologic study designs and analytical methods. This is further enforced by the addition of the meta-analysis chapter. …This book will be valuable to statisticians in applying their discipline to epidemiology. Mark Woodward's excellent second edition will effectively serve post-graduate or advanced undergraduate students studying epidemiology, as well as statisticians or researchers who are regularly confronted with epidemiological questions."
    -Journal of the American Statistical Association

    "This book provides very good coverage of major issues in the design of epidemiological studies, and a decent, but very quick, tour of commonly used statistical models for such studies."
    -Short Book Reviews Publication of the International Statistical Institute, K.S. Brown, University of Waterloo, Canada

    "…Amazingly, Woodward manages to describe quite sophisticated models and analysis with nothing more complicated than summation signs. …I highly recommend it."
    -Statistics in Medicine, 2006

    "The second edition of this concisely written book covers all statistical methods being of relevance for the planning and analysis of epidemiological studies where the author avoids unnecessary mathematical details for the sake of comprehensibility. The presented statistical principles are always carefully discussed in the context of epidemiological concepts, for instance depending on the different study designs. Detailed practical examples coming from real studies as far as possible illustrate their application. …The book can be highly recommended to researchers in epidemiology who want to understand better the statistical principles being typically applied in this field and to statisticians who want to understand more about statistics in epidemiology, but also to graduate students in epidemiology, public health, medical research and statistics."
    -Biometrics, Sept. 2005

    "…I think anyone with an interest in both biostatistics and epidemiology will want a copy this book on their bookshelf… it is a first-rate reference book."

    "I find Professor Woodward's text the most complete and practical introduction to the design and analysis of epidemiological studies I've encountered… an excellent text for either a course introducing epidemiologists to statistical thought and methods or a course introducing statisticians to epidemiological thought and methods… students appreciate having a readable textbook replete with understandable examples and worked exercises…offers a complete introduction to statistical and epidemiological methods in the study of disease in human populations. All of the standard topics are included, and the second edition even has a chapter on meta-analysis."

    "…This book can be used as a text to introduce epidemiological methods to graduate students in statistics who have no background in epidemiology, or vice versa…"

    "…Professor Woodward is to be congratulated on a job well done."
    -Dan McGee, Dept of Statistics, Florida State University

    Downloads Updates


    Resource OS Platform Updated Description Instructions
    FAR.sas All Windows Version November 19, 2004 Floating absolute risk macro for SAS
    Datasets.zip All Windows Version November 19, 2004 Datasets
    Sample size.xls All Windows Version November 19, 2004 Sample size calculator
    SAS.zip All Windows Version November 19, 2004 SAS programs
    Stata.zip All Windows Version November 19, 2004 Stata programs (DO files)
    Corrections.doc Cross Platform August 02, 2005 Errata

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