STA 224: Analysis of Longitudinal Data

Subject: STA 224
Title: Analysis of Longitudinal Data
Units: 4.0
School: College of Letters and Science LS
Department: Statistics STA
Effective Term: 2005 Spring

Learning Activities

  • Lecture - 3.0 hours
  • Discussion/Laboratory - 1.0 hours

Description

Standard and advanced methodology, theory, algorithms, and applications relevant for analysis of repeated measurements and longitudinal data in biostatistical and statistical settings.

Prerequisites

((STA 222, STA 223) or (BST 222, BST 223)); STA 232B; or Consent of Instructor.

Enrollment Restrictions

Suppress CRN in Schedule. 

Cross Listed

Same course as BST 224.

Expanded Course Description

Summary of Course Content: 
Introduction to Longitudinal Studies Design, Explorative analysis, Cohort studies, Growth curves, ANOVA, MANOVA and repeated measurements models. Random Effects Models Linear Mixed Effects Models, Best linear unbiased prediction, Maximum Likelihood, Restricted Maximum Likelihood, Parametric and nonparametric covariance modeling Generalized Linear Mixed Models Estimating Equations, Generalized Estimating Equations, Generalized Linear Mixed Model Advanced Topics Transition and State Space Models, Missing Data and types of Missingness, Joint Modeling of Longitudinal Data and Survival, Functional Data Analysis 

Illustrative Reading: 
References: The course material is extracted from a variety of sources, including journals such as Biometrics, Biometrika, Statistics in Medicine, JASA, Applied Statistics, and the New England Journal of Medicine. Relevant reference books include: Diggle, P., et al. Analysis of Longitudinal Data. Oxford University Press, 2002. G. Verbeke and G. Molenberghs. Linear Mixed Models for Longitudinal Data. Springer, 2000. C.E. McCulloch, S.R. Searle. Generalized Linear and Mixed Models. Wiley, 2001. D.J. Hand, M.J. Crowder. Practical Longitudinal Data Analyis. CRC Press. 1996. Judith D. Singer and John B. Willett. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press 2003. 

Potential Course Overlap: 
None