3rd Edition

Epidemiology Study Design and Data Analysis, Third Edition

By Mark Woodward Copyright 2014
    898 Pages 157 B/W Illustrations
    by Chapman & Hall

    854 Pages 157 B/W Illustrations
    by Chapman & Hall

    Highly praised for its broad, practical coverage, the second edition of this popular text incorporated the major statistical models and issues relevant to epidemiological studies. Epidemiology: Study Design and Data Analysis, Third Edition continues to focus on the quantitative aspects of epidemiological research. Updated and expanded, this edition shows students how statistical principles and techniques can help solve epidemiological problems.

    New to the Third Edition

    • New chapter on risk scores and clinical decision rules
    • New chapter on computer-intensive methods, including the bootstrap, permutation tests, and missing value imputation
    • New sections on binomial regression models, competing risk, information criteria, propensity scoring, and splines
    • Many more exercises and examples using both Stata and SAS
    • More than 60 new figures

    After introducing study design and reviewing all the standard methods, this self-contained book takes students through analytical methods for both general and specific epidemiological study designs, including cohort, case-control, and intervention studies. In addition to classical methods, it now covers modern methods that exploit the enormous power of contemporary computers. The book also addresses the problem of determining the appropriate size for a study, discusses statistical modeling in epidemiology, covers methods for comparing and summarizing the evidence from several studies, and explains how to use statistical models in risk forecasting and assessing new biomarkers. The author illustrates the techniques with numerous real-world examples and interprets results in a practical way. He also includes an extensive list of references for further reading along with exercises to reinforce understanding.

    Web Resource

    A wealth of supporting material can be downloaded from the book’s CRC Press web page, including:

    • Real-life data sets used in the text
    • SAS and Stata programs used for examples in the text
    • SAS and Stata programs for special techniques covered
    • Sample size spreadsheet

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

    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

    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
    EPITAB Commands in Stata

    The Concept of Confounding
    Identification of Confounders
    Assessing Confounding
    Mantel-Haenszel Methods
    The Concept of Interaction
    Testing for Interaction
    Dealing with Interaction
    EPITAB Commands in Stata

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

    Basic Design Concepts
    Basic Methods of Analysis
    Selection of Cases
    Selection of Controls
    The Analysis of Matched Studies
    Nested Case-Control Studies
    Case-Cohort Studies
    Case-Crossover Studies

    Ethical Considerations
    Avoidance of Bias
    Parallel Group Studies
    Cross-Over Studies
    Sequential Studies
    Allocation to Treatment Group
    Trials as Cohorts

    Testing a Mean Value
    Testing a Difference between Means
    Testing a Proportion
    Testing a Relative Risk
    Case-Control Studies
    Complex Sampling Designs
    Concluding Remarks

    Statistical Models
    One Categorical Explanatory Variable
    One Quantitative Explanatory Variable
    Two Categorical Explanatory Variables
    Model Building
    General Linear Models
    Several Explanatory Variables
    Model Checking
    Panel Data
    Non-Normal Alternatives

    Problems with Standard Regression Models
    Logistic Regression
    Interpretation of Logistic Regression Coefficients
    Generic Data
    Multiple Logistic Regression Models
    Tests of Hypotheses
    Dealing with a Quantitative Explanatory Variable
    Model Checking
    Measurement Error
    Case-Control Studies
    Outcomes with Several Levels
    Longitudinal Data
    Binomial Regression
    Propensity Scoring

    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
    Competing Risk
    Poisson Regression
    Pooled Logistic Regression

    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
    Advantages and Limitations of Meta-Analysis

    Association and Prognosis
    Risk Scores from Statistical Models
    Quantifying Discrimination
    The Accuracy of Predictions
    Assessing an Extraneous Prognostic Variable
    Presentation of Risk Scores
    Impact Studies

    The Bootstrap
    Bootstrap Confidence Intervals
    Practical Issues When Bootstrapping
    Further Examples of Bootstrapping
    Bootstrap Hypothesis Testing
    Limitations of Bootstrapping
    Permutation Tests
    Missing Values
    Naive Imputation Methods
    Univariate Multiple Imputation
    Multivariate Multiple Imputation
    When Is It Worth Imputing?

    Appendix A: Materials Available on the Website for This Book
    Appendix B: Statistical Tables
    Appendix C: Additional Data Sets for Exercises



    Mark Woodward is a professor of statistics and epidemiology at the University of Oxford, a professor of biostatistics in the George Institute at the University of Sydney, and an adjunct professor of epidemiology at Johns Hopkins University.

    "This text, like its predecessors, hits the mark. … The author writes extremely well and the text is resplendent with exercises. It would be a crime if Epidemiology: Study Design and Data Analysis were never used as a text! … I wish a text like this had been available for my coursework. Enhancing its value as a text, it will be extremely useful as a reference book for its intended audience—researchers and applied statisticians. … the only excuse for an epidemiologist or applied statistician not to have it on his or her bookshelf is that he or she has not seen or heard of it. Make this book your next purchase!"
    —Gregory E. Gilbert, The American Statistician, November 2014

    Praise for Previous Editions:
    "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