Analysis of correlated data with SAS and R / Mohamed M. Shoukri , Mohammad A. Chaudhary.
Contributor(s): Chaudhary, Mohammad A | Shoukri, M. M. (Mohamed M.). Statistical methods for health sciences.Material type: TextPublisher: Boca Raton : Chapman & Hall/CRC, c2007Edition: 3rd ed.Description: 295 p. : ill. ; 25 cm. +.ISBN: 9781584886198 (acidfree paper); 1584886196; 1584886196 (acidfree paper).Subject(s): Epidemiology -- Statistical methodsDDC classification: 614.4 Online resources: WorldCat details | E-book Fulltext
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|612 MAF 2015 Fundamentals of anatomy & physiology /||612 WAR 2001 Ross and Wilson anatomy and physiology in health and illness /||614.4 GOE 2014 Epidemiology /||614.4 SHA 2007 Analysis of correlated data with SAS and R /||614.4 YOP 2005 Population health :||615.1 Basic & clinical pharmacology /||615.1 ALA 2014 Ansel's pharmaceutical dosage forms and drug delivery systems /|
Originally published: Statistical methods for health sciences. 2nd ed. Boca Raton, Fla. : CRC Press, c1999.
Includes bibliographical references (p. 283-289) and index.
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 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.
Discusses the methodologies used for the analysis of clustered and correlated data. This work includes a chapter devoted to the modeling and analyzing of normally distributed variables under clustered sampling designs. It offers an analysis of correlated count data that focuses on over-dispersion