Analysis of Correlated Data with SAS and R, Third Edition

Published:
May 17, 2007 by Chapman and Hall/CRC - 312 Pages
Author(s):
Mohamed M. Shoukri, King Faisal Specialist Hospital & Research Centre, Riyadh, Saudi Arabia; Mohammad A. Chaudhary, Merck & Co., Inc., North Wales, Pennsylvania, USA

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Hardback
$99.95
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ISBN 9781584886198
Cat# C6196
 

Features

  • Focuses on the analysis of correlated data from epidemiologic and medical investigations
  • Details the statistical analysis of cross-classified data
  • Covers time series, repeated measures, and logistic regression for correlated data in one volume
  • Presents both nonparametric and parametric approaches for the analysis of survival data

    • Summary

    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.

    • Table of 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 × 2 Tables
    Analysis of Several 2 × 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.

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