Association Models in Epidemiology: Study Designs, Modeling Strategies, and Analytic Methods (Hardcover) (ISBN-13: 9781032353401)

Association Models in Epidemiology: Study Designs, Modeling Strategies, and Analytic Methods is written by an epidemiologist for graduate students, researchers, and practitioners who will use regression techniques to analyze data. It focuses on association models rather than prediction models. The book targets students and working professionals who lack bona fide modeling experts but are committed to conducting appropriate regression analyses and generating valid findings from their projects. This book aims to offer detailed strategies to guide them in modeling epidemiologic data.

Features

• Custom-Tailored Models: Discover association models specifically designed for epidemiologic study designs.
• Epidemiologic Principles in Action: Learn how to apply and translate epidemiologic principles into regression modeling techniques.
• Model Specification Guidance: Get expert guidance on model specifications to estimate exposure-outcome associations, accurately controlling for confounding bias.
• Accessible Language: Explore regression intricacies in user-friendly language, accompanied by real-world examples that make learning easier.
• Step-by-Step Approach: Follow a straightforward step-by-step approach to master strategies and procedures for analysis.
• Rich in Examples: Benefit from 120 examples, 77 figures, 86 tables, and 174 SAS(R) outputs with annotations to enhance your understanding.
• Book website located here.

Crafted for two primary audiences, this text benefits graduate epidemiology students seeking to understand how epidemiologic principles inform modeling analyses and public health professionals conducting independent analyses in their work. Therefore, this book serves as a textbook in the classroom and as a reference book in the workplace. A wealth of supporting material is available for download from the book's CRC Press webpage. Upon completing this text, readers should gain confidence in accurately estimating associations between risk factors and outcomes, controlling confounding bias, and assessing effect modification.