Epidemiology : Study Design and Data Analysis, Third Edition

Updated and expanded, this popular text focuses on the quantitative aspects of epidemiological research. It shows students how statistical principles and techniques can help solve epidemiological problems. Along with more exercises and examples using both Stata and SAS, this third edition includes a new chapter on risk scores and clinical decision rules, a new chapter on computer-intensive methods, and new sections on binomial regression models, competing risk, information criteria, propensity scoring, and splines. Supporting materials are available on the book’s CRC Press web page.

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