3rd Edition

Epidemiology
Study Design and Data Analysis, Third Edition




ISBN 9781439839706
Published December 19, 2013 by Chapman and Hall/CRC
898 Pages 157 B/W Illustrations

USD $130.00

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Book Description

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

Table of Contents

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
EPITAB Commands in Stata
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
EPITAB Commands in Stata
Exercises

COHORT STUDIES
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
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
Trials as Cohorts
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

MODELING 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
Splines
Panel Data
Non-Normal Alternatives
Exercises

MODELING 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
Dealing with a Quantitative Explanatory Variable
Model Checking
Measurement Error
Case-Control Studies
Outcomes with Several Levels
Longitudinal Data
Binomial Regression
Propensity Scoring
Exercises

MODELING 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
Competing Risk
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
Advantages and Limitations of Meta-Analysis
Exercises

RISK SCORES AND CLINICAL DECISION RULES
Introduction
Association and Prognosis
Risk Scores from Statistical Models
Quantifying Discrimination
Calibration
Recalibration
The Accuracy of Predictions
Assessing an Extraneous Prognostic Variable
Reclassification
Validation
Presentation of Risk Scores
Impact Studies
Exercises

COMPUTER-INTENSIVE METHODS
Rationale
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?
Exercises

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

Index

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Author(s)

Biography

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.

Reviews

"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