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Course objective:


Studies in which data are collected repeatedly on a sample of individuals over time (or some other condition) are ubiquitous in the health, social, and behavioral sciences; agricultural and biological sciences; education; economics; and business. Questions of interest in the context of such longitudinal data often focus on patterns of change of outcomes of interest over time and on elucidating factors that are associated with patterns of change in relevant populations of individuals. Because the study of change is so pervasive across almost all disciplines, statistical models and methods for the analysis of longitudinal data have become essential tools for practicing statisticians. Moreover, as studies and technologies giving rise to longitudinal data become increasingly complex, development of new methodology continues to be an active research area. This course will provide an overview of statistical models and methods for longitudinal data analysis. Fundamental modeling strategies and methodological developments will be presented in detail and their properties studied via theoretical arguments carried out at a heuristic level. Implementation in R and SAS will also be discussed.

The course will begin with a conceptual framework for thinking about longitudinal data, followed by a brief review of "classical" methods, whose limitations will be highlighted. The rest of the course will focus on more modern methods. Selected advanced topics will also be covered. This course is background for study of areas such as semiparametric theory, functional data analysis, and analysis in the presence of missing data.

Course prerequisites


ST 522, Statistical Theory II, and ST 552, Linear Models and Variance Components, or equivalents. Students should also have been exposed to SAS and R and have reasonable proramming skills.

Course topics


See the class notes for more detailed information