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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