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.

Lyle Gurrin
Prof Lyle Gurrin University of Melbourne, School of Population and Global Health Semester 1
Jessica Kasza portrait
A/Prof Jessica Kasza Monash University, Department of Epidemiology and Preventive Medicine Semester 2
General outline


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

Time commitment

10-12 hours total study time per week

Semester availability

Semester 1 & 2


2 sets of module exercises (1000 words) each worth 20% and 2 written reports (1500 words) worth 30% each

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


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.


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

The BCA acknowledges we live and work on the ancestral lands of Aboriginal and Torres Strait Islander peoples, who have for thousands of generations exchanged knowledge for the benefit of all. We pay our respects to those who have cared and continue to care for Country.