Main description:

Analysis of Correlated Data with SAS and R: 4th edition presents an applied treatment of recently developed statistical models and methods for the analysis of hierarchical binary, count and continuous response data. It explains how to use procedures in SAS and packages in R for exploring data, fitting appropriate models, presenting programming codes and results.

The book is designed for senior undergraduate and graduate students in the health sciences, epidemiology, statistics, and biostatistics as well as clinical researchers, and consulting statisticians who can apply the methods with their own data analyses. In each chapter a brief description of the foundations of statistical theory needed to understand the methods is given, thereafter the author illustrates the applicability of the techniques by providing sufficient number of examples.

The last three chapters of the 4th edition contain introductory material on propensity score analysis, meta-analysis and the treatment of missing data using SAS and R. These topics were not covered in previous editions. The main reason is that there is an increasing demand by clinical researchers to have these topics covered at a reasonably understandable level of complexity.

Mohamed Shoukri is principal scientist and professor of biostatistics at The National Biotechnology Center, King Faisal Specialist Hospital and Research Center and Al-Faisal University, Saudi Arabia. Professor Shoukri's research includes analytic epidemiology, analysis of hierarchical data, and clinical biostatistics. He is an associate editor of the 3Biotech journal, a Fellow of the Royal Statistical Society and an elected member of the International Statistical Institute.

Contents:

ANALYZING GROUP MEANS WHEN THE ANOVA ASSUMPTIONS ARE NOT SATISFIED. STATISTICAL METHODS FOR HOSPITAL EPIDEMIOLOGY. ANALYZING CLUSTERED DATA. ANALYSIS OF CROSS-CLASSIFIED DATA. STATISTICAL ANALYSIS OF CLUSTERED BINARY DATA. MODELLING BINARY OUTCOME DATA. STATISTICAL METHODS FOR PROPENSITY SCORE MATCHING. ANALYSIS OF CLUSTERED COUNT DATA. ANALYSIS OF TIME SERIES WITH APPLICATION TO DETECTION OF DISEASE OUTBREAK. REPEATED MEASURES AND LONGITUDINAL DATA ANALYSIS. DATA ANALYSES WITH MISSING DATA (IMPUTATIONS TECHNIQUES). SURVIVAL DATA ANALYSIS. META ANALYSIS.