Jeffrey Wilson, Ding-Geng Chen, Karl E. Peace

Statistical Analytics for Health Data Science with SAS and R (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book is aimed to compile typical fundamental to advanced statistical methods to be used for health data sciences. This book promotes the applications to health and health-related data. The data and computing programs will be available to facilitate readers' learning experience.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 280
• Erscheinungstermin: 28. März 2023
• Englisch
• ISBN-13: 9781000848823
• Artikelnr.: 67432260

Autorenporträt

Jeffrey Wilson, Ph.D. Professor in Biostatistics and Associate Dean of Research Department of Economics W. P. Carey School of Business, Arizona State University, USA.

Ding-Geng Chen, Ph.D. Professor and Executive Director in Biostatistics College of Health Solutions Arizona State University, USA.

Dr. Karl E. Peace is the Georgia Cancer Coalition Distinguished Cancer Scholar (GCCDCS), Senior Research Scientist and Professor of Biostatistics in the Jiann-Ping Hsu College of Public Health (JPHCOPH) at Georgia Southern University (GSU). He was responsible for establishing the Jiann-Ping Hsu College of Public Health - the first college of public health in the University System of GA (USG). He is the architect of the MPH in Biostatistics - the first-degree program in Biostatistics in the USG and Founding Director of the Karl E. Peace Center for Biostatistics in the JPHCOPH. Dr. Peace holds the Ph.D. in Biostatistics from the Medical College of Virginia, the M.S. in Mathematics from Clemson University, the B.S. in Chemistry from Georgia Southern College, and a Health Science Certificate from Vanderbilt University.

Inhaltsangabe

1. Sampling and Data Collection 2. Measures of Tendency, Spread, Relative
Standing, Association, Belief 3. Statistical Modeling of Mean of Continuous
and Mean of Binary Outcomes 4. Modeling of Continuous and Binary Outcomes
with Factors: One-way and Two-way ANOVA Models 5. Statistical Modeling of
Continuous Outcomes with Continuous Explanatory Factors Linear Regression
Models 6. Modeling Continuous Responses with Categorical and Continuous
Covariates: One-Way Analysis of Covariance (ANCOVA) 7. Statistical Modeling
of Binary Outcome with One or More Covariates: Standard Logistic Regression
Model 8. Generalized Linear Models 9. Modeling Repeated Continuous
Observations using GEE 10. Modeling for Correlated Continuous Responses
with Random-Effects 11. Modeling Correlated Binary Outcomes through
Hierarchical Logistic Regression Models