Wilson J. Statistical Analytics for Health Data Science with SAS and R 2023

Textbook in PDF format

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.
Survey Sampling and Data Collection
Measures of Tendency, Spread, Relative Standing, Association, and Belief
Statistical Modeling of the Mean of Continuous and Binary Outcomes
Modeling of Continuous and Binary Outcomes with Factors: One-Way and Two-Way ANOVA Models
Statistical Modeling of Continuous Outcomes with Continuous Explanatory Factors: Linear Regression Models
Modeling Continuous Responses with Categorical and Continuous Covariates: One-Way Analysis of Covariance (ANCOVA)
Statistical Modeling of Binary Outcomes with One or More Covariates: Standard Logistic Regression Model
Generalized Linear Models
Modeling Repeated Continuous Observations Using GEE
Modeling for Correlated Continuous Responses with Random-Effects
Modeling Correlated Binary Outcomes Through Hierarchical Logistic Regression Models