Quantitative epidemiology [electronic resource]

• 作者： Chen, Xinguang.
• 其他作者：
  • SpringerLink (Online service)
• 其他題名：
  • Emerging topics in statistics and biostatistics.
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Emerging topics in statistics and biostatistics,
• 主題： Epidemiology--Statistical methods. , Epidemiology--Research. , Applied Statistics. , Epidemiology. , Biostatistics. , Public Health.
• ISBN： 9783030838522 (electronic bk.) 、 9783030838515 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: 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.
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• 摘要註: 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, Poisson regression, Cox regression, missing data imputing, and statistical power analysis.
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