overs the spectrum of statistical principles and analytical tools used in epidemiological research
Explains how to design epidemiological studies and how to analyze the data from these studies
Uses data sets taken from real epidemiological investigations and publications to illustrate the concepts and methods
Assumes only a basic statistical background, emphasizing practical methods over complicated proofs
Includes extensive references for further reading as well as end-of-chapter exercises to reinforce understanding
Provides data sets, SAS and Stata programs, and more on the book's CRC Press web page
Solutions manual available upon qualifying course adoption

Summary

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