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Quantitative epidemiology

Chen, Xinguang.

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Quantitative epidemiology

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Quantitative epidemiology/ by Xinguang Chen.
作者:	Chen, Xinguang.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	1 online resource (xx, 336 p.) :ill., digital ;24 cm.
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.
Contained By:	Springer Nature eBook
標題:	Epidemiology - Statistical methods. -
電子資源:	https://doi.org/10.1007/978-3-030-83852-2
ISBN:	9783030838522

Quantitative epidemiology
Chen, Xinguang.

Quantitative epidemiology[electronic resource] /by Xinguang Chen. - Cham :Springer International Publishing :2021. - 1 online resource (xx, 336 p.) :ill., digital ;24 cm. - Emerging topics in statistics and biostatistics,2524-7743. - Emerging topics in statistics and biostatistics..

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.

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.

ISBN: 9783030838522

Standard No.: 10.1007/978-3-030-83852-2doiSubjects--Topical Terms:

616294
Epidemiology
--Statistical methods.

LC Class. No.: RA652.2.M3 / C44 2021

Dewey Class. No.: 614.40727

Quantitative epidemiology
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520 $a 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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