Advanced Statistical Analytics for Health Data Science with SAS and R

About this book

In recent years, there has been a growing emphasis on making statistical methods and analytics accessible to health data science researchers and students. Following the first book on "Statistical Analytics for Health Data Science with SAS and R" (2023, www.routledge.com/9781032325620), this book serves as a comprehensive reference for health data scientists, bridging fundamental statistical principles with advanced analytical techniques. By providing clear explanations of statistical theory and its application to real- world health data, we aim to equip researchers with the necessary tools to navigate the evolving landscape of health data science. Designed for advanced-level data scientists, this book 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. A key feature of this book is its emphas…