CiNii 図書 - Analysis of correlated data with SAS and R

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.

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.

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Analysis of correlated data with SAS and R

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