Statistical Analytics for Health Data Science with SAS and R Set (Chapman & Hall/CRC Biostatistics Series)

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 statist