Analysis of Correlated Data with SAS and R

The fifth edition provides cutting-edge methodologies for analysing clustered and correlated data, incorporating new topics and advancements in statistical modelling and machine learning. With extensive examples, real-world datasets, and practical codes for R and SAS, this book equips readers with the tools to master advanced data analytics. The fifth edition of Analysis of Correlated Data with SAS and R provides cutting‑edge methodologies for analyzing clustered and correlated data, incorporating new topics and advancements in statistical modeling and machine learning. This updated edition introduces five new topics: (1) simultaneous testing of equality of correlated means and variances, (2) beta‑binomial regression model for the analysis of binomial data with overdispersion, (3) modeling count data with inflated zero class and zero‑truncated data, (4) analysis of multivariate time series, and (5) network meta‑analysis. Additionally, a new chapter on integrating statistical methods with machine learning algorithms offers deeper insights into biomedical data analysis. With extensive examples, real‑world datasets, and practical codes for R and SAS, this book equips readers with the tools to master advanced data analytics. Designed for researchers, students, and professionals in biostatistics, biomedical data analysis, and applied statistics, this book is ideal for those seeking to enhance their understanding of computing as an essential part of the practice of statistics and machine learning. It is particularly suited for individuals using R or SAS for data analysis and reporting. Key Features: Provides extensive examples to illustrate methodologies Focuses on data analytics of clustered data, regardless of whether the clusters are due to natural sampling processes or repeated measures over time Provides a substantial library of Excel datasets for practical application Provides detailed R and SAS codes to demonstrate data analysis techniques