Main description:

Previously known as Statistical Methods for Health Sciences, this bestselling resource is one of the first books to discuss the methodologies used for the analysis of clustered and correlated data. While the fundamental objectives of its predecessors remain the same, Analysis of Correlated Data with SAS and R, Third Edition incorporates several additions that take into account recent developments in the field.

New to the Third Edition
The introduction of R codes for almost all of the numerous examples solved with SAS

A chapter devoted to the modeling and analyzing of normally distributed variables under clustered sampling designs

A chapter on the analysis of correlated count data that focuses on over-dispersion

Expansion of the analysis of repeated measures and longitudinal data when the response variables are normally distributed

Sample size requirements relevant to the topic being discussed, such as when the data are correlated because the sampling units are physically clustered or because subjects are observed over time

Exercises at the end of each chapter to enhance the understanding of the material covered

An accompanying CD-ROM that contains all the data sets in the book along with the SAS and R codes

Assuming a working knowledge of SAS and R, this text provides the necessary concepts and applications for analyzing clustered and correlated data.

Contents:

PREFACE TO THE FIRST EDITION
PREFACE TO THE SECOND EDITION
PREFACE TO THE THIRD EDITION

ANALYZING CLUSTERED DATA
Regression Analysis for Clustered Data
Generalized Linear Models
Fitting Alternative Models for Clustered Data

ANALYSIS OF CROSS-CLASSIFIED DATA
Measures of Association in 2 x 2 Tables
Analysis of Several 2 x 2 Contingency Tables
Analysis of 1:1 Matched Pairs
Statistical Analysis of Clustered Binary Data
Sample Size Requirements for Clustered Binary Data
Discussion

MODELING BINARY OUTCOME DATA
The Logistic Regression Model
Modeling Correlated Binary Outcome Data
Logistic Regression for Case-Control Studies
Sample-Size Calculations for Logistic Regression

ANALYSIS OF CLUSTERED COUNT DATA
Poisson Regression
Model Inference and Goodness of Fit
Over-Dispersion in Count Data
Count Data Random Effects Models
Other Models

ANALYSIS OF TIME SERIES
Simple Descriptive Methods
Fundamental Concepts in the Analysis of Time Series
Models for Stationary Time Series
ARIMA Models
Forecasting
Modeling Seasonality with ARIMA: The Condemnation Rates Series Revisited

REPEATED MEASURES AND LONGITUDINAL DATA ANALYSIS
Methods for the Analysis of Repeated Measures Data
Mixed Linear Regression Models
Examples Using the SAS Mixed and GLIMMIX Procedures

SURVIVAL DATA ANALYSIS
Examples
Estimating the Survival Probabilities
Modeling Correlated Survival Data
Sample Size Requirements for Survival Data

REFERENCES

INDEX

Introductions appear at the beginning of each chapter.