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