EPM5008 Longitudinal and correlated data analysis
Faculty of Medicine, Nursing and Health Sciences
EPM5008 Longitudinal and correlated data analysis is a level 5, 6-credit-point, postgraduate unit from the Faculty of Medicine, Nursing and Health Sciences, offered in 2020 in Semester 1 at Alfred Hospital. It needs EPM5014, EPM5003, MPH5040, EPM5004, EPM5009 and EPM5002.
- Credit points
- 6
- Offered in 2020
- Semester 1
- Alfred Hospital
This is the 2020 handbook entry. See the 2027 entry.
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Requisites
Before EPM5008
Prerequisites
Pass these before you enrol.
- EPM5014Probability and distribution theoryNo reviews yet
- EPM5003Principles of statistical inferenceNo reviews yet
- MPH5040Introductory epidemiologyNo reviews yet
- EPM5004Linear modelsNo reviews yet
- EPM5009Categorical data and generalised linear modelsNo reviews yet
- EPM5002Mathematical background for biostatisticsNo reviews yet
After EPM5008
No unit lists EPM5008 as a prerequisite in the 2020 handbook.
Enrolment rules
Corequisite: This unit is only available to students enrolled in the Graduate Certificate, Graduate Diploma or Masters of Biostatistics.
Overview
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.
Offerings in 2020
| Teaching period | Campus | Mode |
|---|---|---|
| First semester | Alfred Hospital | De |
Learning outcomes
When you finish this unit, you should be able to:
- 1
Recognise the existence of correlated or hierarchical data structures, and describe the limitations of standard methods in these settings.
- 2
Develop and analytically describe an appropriate model for longitudinal or correlated data based on unit matter considerations.
- 3
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.
- 4
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.
Contacts
- Chief Examiners
- Associate Professor Andrew Forbes
- Unit Coordinators
- Associate Professor Andrew Forbes