This course will provide an introduction to the principles and methods for the analysis of longitudinal data. Conditional and random effect modeling approaches to regression analysis will be covered, as well as semiparametric methods based on generalized estimating equations. Supporting statistical theory will be given at a level appropriate for a senior undergraduate student in Statistics. The importance of model assessment and parameter interpretation will be emphasized. Problems will be motivated by applications in epidemiology, clinical medicine, health services research, and disease natural history studies. Students will be required to think critically about appropriate strategies for data analysis. Analysis will be carried out with SAS and S-PLUS/R software and the importance of providing clear interpretations to data analyses will be emphasized. [Note: First offering will be 2014. Offered: W] Prereq: STAT 431
