Longitudinal and Correlated Data (LCD)

To enable students to apply appropriate methods to the analysis of data arising from longitudinal (repeated measures) epidemiological or clinical studies, and from studies with other forms of clustering (cluster sample surveys, cluster randomised trials, family studies) that will produce non-exchangeable outcomes.



COORDINATORS:
Lyle Gurrin
Prof Lyle Gurrin University of Melbourne, School of Population and Global Health Semester 1
General outline

Prerequisites

Epidemiology, Mathematical Foundations for Biostatistics, Principles of Statistical Inference, Regression Modeling for Biostatistics 1

Time commitment

8-12 hours total study time per week

Semester availability

Semester 1

Assessment

Assignments 100% (two major assignments worth 30% each (8 hours) and 5 shorter assignments each worth 8%.

Prescribed Texts

Recommended – not compulsory: Fitzmaurice G, Laird N, Ware J. Applied Longitudinal Analysis. John Wiley and Sons, 2011.

Special Computer Requirements

R or Stata statistical software

Content

Paired data; the effect of non-independence on comparisons within and between clusters of observations; methods for continuous outcomes: normal mixed effects (hierarchical or multilevel) models and generalised estimating equations (GEE); role and limitations of repeated measures ANOVA; methods for discrete data: GEE and generalized linear mixed models (GLMM); methods for count data.

Resources

Course notes, online mini-lecture videos, online tutorials, discussion board