Full 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
Contents
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.
