Postgraduate - UnitEPM5008 - Longitudinal and correlated data analysis

This unit entry is for students who completed this unit in 2015 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

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6 points, SCA Band 2, 0.125 EFTSL

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

Level	Postgraduate
Faculty	Faculty of Medicine, Nursing and Health Sciences
Organisational Unit	Department of Epidemiology and Preventive Medicine
Offered	Alfred Hospital First semester 2015 (Off-campus)
Coordinator(s)	Professor Andrew Forbes and Associate Professor John Carlin

Synopsis

This unit will develop statistical models for longitudinal and correlated data in medical research. The concept of hierarchical data structures will be developed, together with simple numerical and analytical demonstrations of the inadequacy of standard statistical methods. Normal-theory model and statistical procedures i.e. mixed linear models are explored using SAS or Stata statistical software packages. Extension to non-normal outcomes emphasising clinical research question. Case studies contrast generalised estimating equations and generalised linear mixed models. Limitations of traditional repeated measures analysis of variance and non-exchangeable models.

Outcomes

Upon successful completion of this unit, students should be able to:

recognise the existence of correlated or hierarchical data structures, and describe the limitations of standard methods in these settings;
develop and analytically describe an appropriate model for longitudinal or correlated data based on unit matter considerations;
be proficient at using a statistical software package (e.g. Strata or SAS) to properly model and perform computations for longitudinal data analyses, and to correctly interpret results; and
express the results of statistical analyses of longitudinal data in language suitable for communication to medical investigators or publication in biomedical or epidemiological journal articles.