Statistical Analytics for Health Data Science with SAS and R

Inbunden, Engelska, 2023

Del i serien Chapman & Hall/CRC Biostatistics Series

1 245 kr

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Beskrivning

This book aims to compile typical fundamental-to-advanced statistical methods to be used for health data sciences. Although the book promotes applications to health and health-related data, the models in the book can be used to analyze any kind of data. The data are analyzed with the commonly used statistical software of R/SAS (with online supplementary on SPSS/Stata). The data and computing programs will be available to facilitate readers’ learning experience. There has been considerable attention to making statistical methods and analytics available to health data science researchers and students. This book brings it all together to provide a concise point-of-reference for the most commonly used statistical methods from the fundamental level to the advanced level. We envisage this book will contribute to the rapid development in health data science. We provide straightforward explanations of the collected statistical theory and models, compilations of a variety of publicly available data, and illustrations of data analytics using commonly used statistical software of SAS/R. We will have the data and computer programs available for readers to replicate and implement the new methods. The primary readers would be applied data scientists and practitioners in any field of data science, applied statistical analysts and scientists in public health, academic researchers, and graduate students in statistics and biostatistics. The secondary readers would be R&D professionals/practitioners in industry and governmental agencies. This book can be used for both teaching and applied research.

Produktinformation

Utgivningsdatum:2023-03-28
Mått:156 x 234 x 22 mm
Vikt:560 g
Format:Inbunden
Språk:Engelska
Serie:Chapman & Hall/CRC Biostatistics Series
Antal sidor:258
Förlag:Taylor & Francis Ltd
ISBN:9781032325620

Utforska kategorier

Mer om författaren

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.

Recensioner i media

"In summary, Statistical Analytics for Health Data Science with SAS and R excels in demystifying intricate statistical concepts and offers both theoretical grounding and practical experience. Whether you are an applied data scientist, a graduate student, or a public health researcher, this work by Willson, Chen, and Peace is an invaluable asset for learning and applying statistics in real-world settings."Ali Rahnavard, The George Washingotn University USA, Journal of the American Statistical Association, 2023.

Innehållsförteckning

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