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Epidemiology

Study Design and Data Analysis, Third Edition

By Mark Woodward

Chapman and Hall/CRC – 2013 – 898 pages

Series: Chapman & Hall/CRC Texts in Statistical Science

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    978-1-43-983970-6
    December 19th 2013

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

Reviews

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

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

Author Bio

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.

Name: Epidemiology: Study Design and Data Analysis, Third Edition (Hardback)Chapman and Hall/CRC 
Description: By Mark Woodward. 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...
Categories: Statistics for the Biological Sciences, Statistical Theory & Methods, Epidemiology