Statistical Analytics for Health Data Science with SAS and R Set
1st Edition
Published on 14. November 2025
Book
510 pages
978-1-041-08987-2 (ISBN)
Description
Statistical Analytics for Health Data Science with SAS and R Set compiles fundamental statistical principles with advanced analytical techniques and covers a wide range of statistical methodologies including models for longitudinal data with time-dependent covariates, multi-membership mixed-effects models, statistical modeling of survival data, Bayesian statistics, joint modeling of longitudinal and survival data, nonlinear regression, statistical meta-analysis, spatial statistics, structural equation modeling, latent growth curve modeling, causal inference and propensity score analysis.
With an emphasis on real-world applications, the books integrate publicly available health datasets and provide case studies from a variety of health applications demonstrating how statistical methods can be applied to solve critical problems in health science. To support hands-on learning, they offer implementation guidance using SAS and R, ensuring that readers can replicate analyses and apply statistical techniques to their own research. Step-by-step computational examples facilitate reproducibility and deeper exploration of statistical models.
Statistical Analytics for Health Data Science with SAS and R has been expanded from eleven chapters to twenty-three chapters in two textbooks and is intended for data scientists and applied statisticians while also being useful as a comprehensive reference for graduate students, academic researchers and public health professionals that will help them gain expertise in advance data-driven decision-making and contribute to evidence-based health research.
Key Features:
Extensive compilation of commonly used statistical methods from fundamental to advanced level
Straightforward explanations of the collected statistical theory and models
Illustration of data analytics using commonly used statistical software of SAS/R and real health data
Handbook for data scientists and applied statisticians in health data science
With an emphasis on real-world applications, the books integrate publicly available health datasets and provide case studies from a variety of health applications demonstrating how statistical methods can be applied to solve critical problems in health science. To support hands-on learning, they offer implementation guidance using SAS and R, ensuring that readers can replicate analyses and apply statistical techniques to their own research. Step-by-step computational examples facilitate reproducibility and deeper exploration of statistical models.
Statistical Analytics for Health Data Science with SAS and R has been expanded from eleven chapters to twenty-three chapters in two textbooks and is intended for data scientists and applied statisticians while also being useful as a comprehensive reference for graduate students, academic researchers and public health professionals that will help them gain expertise in advance data-driven decision-making and contribute to evidence-based health research.
Key Features:
Extensive compilation of commonly used statistical methods from fundamental to advanced level
Straightforward explanations of the collected statistical theory and models
Illustration of data analytics using commonly used statistical software of SAS/R and real health data
Handbook for data scientists and applied statisticians in health data science
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
College/higher education
Academic
Illustrations
26 farbige Abbildungen, 2 s/w Photographien bzw. Rasterbilder, 40 s/w Zeichnungen, 26 farbige Zeichnungen, 59 s/w Tabellen, 42 s/w Abbildungen
59 Tables, black and white; 26 Line drawings, color; 40 Line drawings, black and white; 2 Halftones, black and white; 26 Illustrations, color; 42 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
Weight
1140 gr
ISBN-13
978-1-041-08987-2 (9781041089872)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Dr. Jeffrey Wilson is a Professor of Statistics and Biostatistics and serves as the Associate Dean of Research Department of Economics W. P. Carey School of Business, Arizona State University, USA. His research focuses on statistical analysis of binary correlated data, and he has authored numerous peer-reviewed articles in the field. He has received several prestigious honors, including the 2024 Dr. Martin Luther King Jr. Faculty.
Dr. Ding-Geng Chen is an elected fellow of the American Statistical Association and an elected member of the International Statistical Institute. Currently he is the executive director and professor in biostatistics at the College of Health Solutions, Arizona State University. Dr. Chen has more than 250 referred professional publications and co-authored and co-edited 42 books on clinical trial methodology, meta-analysis, data science, causal inference, and public health statistics.
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.
Dr. Ding-Geng Chen is an elected fellow of the American Statistical Association and an elected member of the International Statistical Institute. Currently he is the executive director and professor in biostatistics at the College of Health Solutions, Arizona State University. Dr. Chen has more than 250 referred professional publications and co-authored and co-edited 42 books on clinical trial methodology, meta-analysis, data science, causal inference, and public health statistics.
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.
Content
Statistical Analytics for Health Data Science with SAS and R
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
Advanced Statistical Analytics for Health Data Science with SAS and R
12. Marginal Models for Binary Longitudinal Outcomes with Time-Dependent Covariates. 13. Multiple Models for Binary Longitudinal Mixed-Model Effects. 14. Statistical Modeling of Survival Data Statistical. 15. Statistical Modeling with Bayesian Paradigm. 16. Jointly Modeling to Analyze Longitudinal and Survival Data with Bayesian Approach. 17. Nonlinear Regression. 18. Statistical Meta-Analysis. 19. Spatial Statistical Analysis. 20. Structural Equation Modeling. 21. Longitudinal Data Analysis and Latent Growth Curve Modelling. 22. Latent Growth Mixture Joint Modeling in Intervention Research. 23. Causal Inference and Propensity Score Analysis.
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
Advanced Statistical Analytics for Health Data Science with SAS and R
12. Marginal Models for Binary Longitudinal Outcomes with Time-Dependent Covariates. 13. Multiple Models for Binary Longitudinal Mixed-Model Effects. 14. Statistical Modeling of Survival Data Statistical. 15. Statistical Modeling with Bayesian Paradigm. 16. Jointly Modeling to Analyze Longitudinal and Survival Data with Bayesian Approach. 17. Nonlinear Regression. 18. Statistical Meta-Analysis. 19. Spatial Statistical Analysis. 20. Structural Equation Modeling. 21. Longitudinal Data Analysis and Latent Growth Curve Modelling. 22. Latent Growth Mixture Joint Modeling in Intervention Research. 23. Causal Inference and Propensity Score Analysis.