Focuses on integrating epidemiologic principles with statistical modeling methods in such a way that the principles inform the choice of modeling strategy and covariate selection.
Association Models in Epidemiology: Study Design, 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 a bona fide modeling expert 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, controlling for confounding bias accurately.
- 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® outputs with annotations to enhance your understanding.
Crafted for two primary audiences, this book 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 books CRC Press webpage. Upon completing this book, readers should gain confidence in accurately estimating associations between risk factors and outcomes, controlling confounding bias, and assessing effect modification.
1. Association Models in Analytic Epidemiologic Research: Principles and Methods.
2. Modeling for Cohort Studies: Incidence Rate Ratio, Risk Ratio, and Risk Difference.
3. Modeling for Cohort Studies: Time-to-Event Outcome.
4. Modeling for Cohort Studies: Propensity Score Method.
5. Modeling for Traditional Case-Control Studies.
6. Modeling for Matched Case-Control Studies.
7. Modeling for Population-Based Case-Control Studies.
8. Modeling for Cross-Sectional Studies.
9. Modeling for Ecologic Studies.
10. Spline Regression Models: Beyond Linearity and Categorization.
Hongjie Liu is professor of epidemiology at the School of Public Health, University of Maryland, College Park. He earned his doctoral degree in epidemiology from the School of Public Health at the University of California, Los Angeles (UCLA). His research focuses on the epidemiology of infectious diseases and research methodology. He has served as the principal investigator, co-investigator, and biostatistics consultant in over 30 research projects and published 125 peer-reviewed papers. Over the past two decades, Dr. Liu has taught intermediate and advanced epidemiology to master's and doctoral students. His courses emphasize the integration of epidemiologic principles with regression techniques.
Daugiau apie elektronines knygas