Longitudinal and Correlated Data

Subject POPH90123 (2013)

Note: This is an archived Handbook entry from 2013.

Credit Points:	12.50
Level:	9 (Graduate/Postgraduate)
Dates & Locations:	This subject is not offered in 2013.
Time Commitment:	Contact Hours: None Total Time Commitment: 8-12 hours total study time per week
Prerequisites:	- Subject Study Period Commencement: Credit Points: POPH90016 Epidemiology Not offered in 2013 12.50 POPH90015 Mathematics Background for Biostatistics Semester 2 12.50 POPH90017 Principles of Statistical Inference Semester 1 12.50 POPH90120 Linear Models Semester 2 12.50 POPH90121 Categorical Data & GLMs Not offered in 2013 12.50 POPH90148 Probability and Distribution Theory Not offered in 2013 12.50
Corequisites:	None
Recommended Background Knowledge:	None
Non Allowed Subjects:	None
Core Participation Requirements:	For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit website.

Contact

Professor Andrew Forbes, Monash University
Professor John Carlin, Melbourne School of Populaton Health, University of Melbourne

Biostatistics Collaboration of Australia
Email: bca@ctc.usyd.edu.au
Website: www.bca.edu.au

Academic Programs Office
Melbourne School of Population Health
Tel: +61 3 8344 9339
Fax: +61 3 8344 0824
Email: sph-gradinfo@unimelb.edu.au

Subject Overview:	Topics covered: 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.
Objectives:	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.
Assessment:	Two written assignments to be submitted during semester worth 40% each (approx 12 hours work each) Four practical exercises due throughout the semester worth 5% each (approx 6 hrs work each)
Prescribed Texts:	None Recommended Text: Fitzmaurice G, Laird N, Ware J. Applied Longitudinal Analysis. John Wiley and Sons, 2004. (ISBN 978-0-471-21487-8) Resources Provided to Students: Printed course notes and assignment material by mail, email, and online interaction facilities. Special Computer Requirements: Stata and SAS statistical software
Breadth Options:	This subject is not available as a breadth subject.
Fees Information:	Subject EFTSL, Level, Discipline & Census Date
Generic Skills:	Independent problem solving; facility with abstract reasoning; clarity of written expression; sound communication of technical concepts.
Links to further information:	http://www.sph.unimelb.edu.au
Notes:	This subject is not available in the Master of Public Health.
Related Course(s):	Master of Biostatistics Postgraduate Certificate in Biostatistics Postgraduate Diploma in Biostatistics

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