## Longitudinal Data AnalysisLongitudinal data analysis for biomedical and behavioral sciences This innovative book sets forth and describes methods for the analysis of longitudinaldata, emphasizing applications to problems in the biomedical and behavioral sciences. Reflecting the growing importance and use of longitudinal data across many areas of research, the text is designed to help users of statistics better analyze and understand this type of data. Much of the material from the book grew out of a course taught by Dr. Hedeker on longitudinal data analysis. The material is, therefore, thoroughly classroom tested and includes a number of features designed to help readers better understand and apply the material. Statistical procedures featured within the text include: * Repeated measures analysis of variance * Multivariate analysis of variance for repeated measures * Random-effects regression models (RRM) * Covariance-pattern models * Generalized-estimating equations (GEE) models * Generalizations of RRM and GEE for categorical outcomes Practical in their approach, the authors emphasize the applications of the methods, using real-world examples for illustration. Some syntax examples are provided, although the authors do not generally focus on software in this book. Several datasets and computer syntax examples are posted on this title's companion Web site. The authors intend to keep the syntax examples current as new versions of the software programs emerge. This text is designed for both undergraduate and graduate courses in longitudinal data analysis. Instructors can take advantage of overheads and additional course materials available online for adopters. Applied statisticians in biomedicine and the social sciences can also use the book as a convenient reference. |

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Useful for designing clinical trials. -Lewis Hsu, MD, PhD. Pediatrics Dept. University of Illinois.

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ANOVA assumed assumption autocorrelated average baseline Bayes estimates between-subjects chapter cluster covariance matrix CPMs cumulative logits dataset deﬁned denoted dependent variable described diagonal dichotomous dropout equal equation example Figure ﬁrst ﬁxed effects function GEE models Gibbons given HDRS Hedeker indicates individual inﬂuence interaction intraclass correlation likelihood ratio test linear trend logistic regression logistic regression model Longitudinal Data Analysis marginal MCAR mean methods missing data missingness mixed-effects regression models model ﬁt multivariate normally distributed observed ordinal orthogonal polynomial outcome parameter estimates patients pattern-mixture placebo Poisson regression population post-intervention probability probit proportional odds quadratic random effects random intercept model random subject effects regression model regressors repeated measures represents response sample signiﬁcant signiﬁcantly slope speciﬁc standard errors Statistical suicide rate survival analysis Table timepoints transplantation treatment trend components univariate values variance—covariance matrix variance—covariance structure vector Wald test week yields zero ZIP model