ST732 - Longitudinal Data Analysis
- Prerequisites: ST 702 and ST 705
- Term & Frequency: Every Spring
- Student Audience: PhD students in statistics and related fields. The course will count toward ST PhD electives. Background in the material in ST 702, Statistical Theory II (Inference) and ST 705, Linear Models and Variance Components, will be helpful.
- Credit: 3 credits
- Recent Texts: Lecture notes prepared by Marie Davidian
- Recent Instructors: Marie Davidian
- Background and Goals: 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.
- Content: Introduction to modeling longitudinal data; Population-averaged vs. subject-specific modeling; Classical repeated measures analysis of variance methods and drawbacks; Review of M-estimation and estimating equations; Population-averaged linear models; Linear mixed effects models; Maximum likelihood, restricted maximum likelihood, and large sample theory; Review of nonlinear and generalized linear regression models; Population-averaged models and generalized estimating equations; Nonlinear and generalized linear mixed effects models; Implications of missing data; Advanced topics (such as multi-level hierarchical models, semi-nonparametric mixed effects models, models for multivariate longitudinal outcomes; relaxing assumptions on random effects in mixed effects models). Implementation in SAS and R.
- Subsequent Courses: This course is background for study of areas such as semiparametric
theory, functional data analysis, and analysis in the presence of
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