Analysis of Repeated Measures Data

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This book offers a wide array of statistical techniques tailored to the emerging needs in repeated measures analysis. It provides an extensive overview of generalized linear model extensions for the bivariate exponential family of distributions, marking a significant advancement in the analysis of repeated measures data. The increasing demand for statistical models addressing correlated outcomes stems from two primary associations: between outcomes themselves and between explanatory variables and outcomes. The text systematically tackles key issues in modeling repeated measures data, emphasizing factors crucial for estimating relationships between covariates and outcome variables in correlated data. New methodologies are introduced to confront contemporary challenges, utilizing Markov models of various orders for conditional models and developing joint models using both marginal-conditional and joint probabilities. The book also highlights extended semi-parametric models for continuous failure time data, broadening the scope of outcome variables relevant to researchers across disciplines. Additionally, it addresses the analysis of repeated measures data within the competing risk framework, increasingly vital in survival analysis, reliability, and actuarial science. Each chapter includes practical guidance on analyses, supplemented by newly developed R packages and SAS codes, making it an essential resource for researchers and