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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.
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.
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