Buch, Englisch, 336 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 540 g
Buch, Englisch, 336 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 540 g
Reihe: Emerging Topics in Statistics and Biostatistics
ISBN: 978-3-030-83854-6
Verlag: Springer International Publishing
This book is designed to train graduate students across disciplines within the fields of public health and medicine, with the goal of guiding them in the transition to independent researchers. It focuses on theories, principles, techniques, and methods essential for data processing and quantitative analysis to address medical, health, and behavioral challenges. Students will learn to access to existing data and process their own data, quantify the distribution of a medical or health problem to inform decision making; to identify influential factors of a disease/behavioral problem; and to support health promotion and disease prevention. Concepts, principles, methods and skills are demonstrated with SAS programs, figures and tables generated from real, publicly available data. In addition to various methods for introductory analysis, the following are featured, including 4-dimensional measurement of distribution and geographic mapping, multiple linear and logistic regression, Poissonregression, Cox regression, missing data imputing, and statistical power analysis.
Zielgruppe
Upper undergraduate
Autoren/Hrsg.
Fachgebiete
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Public Health, Gesundheitsmanagement, Gesundheitsökonomie, Gesundheitspolitik
Weitere Infos & Material
1. Introduction to Quantitative Epidemiology.- 2. Characters, Variables, Data, and Information.- 3. Quantitative Descriptive Epidemiology.- 4. Causal Exploration with Bivariate Analysis.- 5. Confirmation with Multiple Linear Regression.- 6. Multivariate Analyses of Categorical and Counting Data.- 7. Multivariate Analysis of Time to Event Data.- 8. Simultaneous Analysis of Two Correlated Predictors.- 9. Special Issues with Quantitative Epidemiology.- 10. Power Analysis.