Course coordinator
Dr Michael Waller
Course overview
- Study period
- Semester 2, 2025 (28/07/2025 - 22/11/2025)
- Study level
- Postgraduate Coursework
- Location
- External
- Attendance mode
- Online
- Units
- 2
- Administrative campus
- Herston
- Coordinating unit
- Public Health School
This course covers methods to analyse data from longitudinal (repeated measures) epidemiological or clinical studies; paired data; the effect of non-independence on comparisons within and between clusters of observations; methods for continuous outcomes: normal mixed effects.
This course is part of the Biostatistics Collaboration of Australia. If you are not enrolled in a Biostatistics program at UQ, please contact the Program Director, Dr Michael Waller, to seek permission before enrolling.
Course requirements
Prerequisites
You'll need to complete the following courses before enrolling in this one:
STAT7607 + STAT7608 or STAT7618
Course contact
Course staff
Lecturer
Dr Michael Waller
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
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.
ﾠ
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Recognise the existence of correlated or hierarchical data structures, and describe the limitations of standard methods in these settings;
LO2.
Develop and analytically describe an appropriate model for longitudinal or correlated data based on subject matter considerations;
LO3.
Be proficient at using statistical software packages (Stat & SAS) to properly model and perform computations for longitudinal data analyses, and to correctly interpret results;
LO4.
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.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Paper/ Report/ Annotation |
Short-answer questions, Modules 1-2
|
20% |
26/08/2024 11:59 pm |
| Paper/ Report/ Annotation |
Report, Modules 1-3
|
30% |
16/09/2024 11:59 pm |
| Paper/ Report/ Annotation |
Short-answer questions, Module 4-5
|
20% |
14/10/2024 11:59 pm |
| Paper/ Report/ Annotation |
Report, Modules 4-6
|
30% |
8/11/2024 11:59 pm |
Assessment details
Short-answer questions, Modules 1-2
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 20%
- Due date
26/08/2024 11:59 pm
Task description
Refer to study guide
Submission guidelines
Refer to study guide
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
Report, Modules 1-3
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 30%
- Due date
16/09/2024 11:59 pm
Task description
Refer to study guide
Submission guidelines
Refer to study guide
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
Short-answer questions, Module 4-5
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 20%
- Due date
14/10/2024 11:59 pm
Task description
Refer to study guide
Submission guidelines
Refer to study guide
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
Report, Modules 4-6
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 30%
- Due date
8/11/2024 11:59 pm
Task description
Refer to study guide
Submission guidelines
Refer to study guide
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Description |
|---|---|
| 1 (Low Fail) |
Absence of evidence of achievement of course learning outcomes. Course grade description: (typically 0-19%) |
| 2 (Fail) |
Minimal evidence of achievement of course learning outcomes. Course grade description: (typically 20-44%) |
| 3 (Marginal Fail) |
Demonstrated evidence of developing achievement of course learning outcomes Course grade description: (typically 45-49%) |
| 4 (Pass) |
Demonstrated evidence of functional achievement of course learning outcomes. Course grade description: (typically 50-64%) |
| 5 (Credit) |
Demonstrated evidence of proficient achievement of course learning outcomes. Course grade description: (typically 65-74%) |
| 6 (Distinction) |
Demonstrated evidence of advanced achievement of course learning outcomes. Course grade description: (typically 75-84%) |
| 7 (High Distinction) |
Demonstrated evidence of exceptional achievement of course learning outcomes. Course grade description: (typically 85-100%) |
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Supplementary Assessment: The final grade awarded will be based on the results of the supplementary assessment only, and a passing grade will be awarded if, and only if, the student receives at least 50% of the marks on the supplementary assessment.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Library resources are available on the UQ Library website.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks |
Tutorial |
Module 1 Introduction: paired data and simple clustering Recognise situations where there is clustering in a data structure Appreciate the impact that clustering can have on standard analytical methods for uncorrelated data Recognise when research questions involve between-cluster or within-cluster comparisons Formulate an appropriate regression model, incorporating random effects, for paired data and understand the interpretation of parameters in this model Understand the principles of generalised estimating equations (GEE) and be able to manually perform its "behind-the-scenes" calculations in a simple model for the means of a continuous outcome variable when there is within-cluster correlation but no covariates gain an introduction to basic commands in Stata and/or R for performing correlated data analyses. |
Tutorial |
Module 2: Exploratory analysis and simple methods using summary measures Use graphical methods to explore patterns in longitudinal data Compute simple summary statistics to describe patterns in longitudinal data Convert longitudinal data from "long" to "wide" format and vice versa Understand the distinction between longitudinal and cross-sectional relationships Appreciate the impact that correlated data has on sample size calculations in the case of cluster randomised trials |
|
Tutorial |
Module 3: Modelling longitudinal continuous outcomes: estimating equations (marginal model) approach Understand how conventional multiple linear regression (ordinary least squares) can be extended to model the mean of a continuous outcome variable while allowing for correlated observations with clusters Appreciate how such models can be specified simply via matrix representations Understand the form of simple correlation structures and recognise situations when they are likely to be appropriate Understand the general concept, construction and representation of weighted last squares estimators as solutions to "estimating equations" Understand how residuals are used to estimate within-cluster correlation parameters and the general method of solution of estimating equations Recognise the usefulness and understand the construction of robust variance estimators |
|
Tutorial |
Module 4: Modelling longitudinal continuous outcomes: mixed models approach Understand how random effects can be incorporated into regression models to represent certain correlation structures within each cluster Appreciate the difference between these likelihood-based mixed-model methods and those of marginal models for the mean (as in Module 3) Be proficient in the us of Stata and/or R commands to estimate parameters of mixed models with random intercepts and slopes and a variety of correlation structures Be able to apply criteria to assist in selection of an appropriate correlation structure |
|
Tutorial |
Module 5: Methods for discrete data: GEE and generalized linear mixed models (GLMM) Appreciate how the generalised estimating equation framework is an extension of logistic regression to accommodate within-cluster correlation with binary data Appreciate how likelihood-based inferences are possible using generalised linear mixed models incorporating random effects, and of general issues involved in their computation Understand the difference in interpretation of regression parameters between GEE and generalised linear mixed modelling approaches Be proficient in the fitting of both sorts of models in Stata and/or R |
|
Tutorial |
Module 6: Methods for longitudinal & correlated count data Appreciate how the generalised estimating equation framework can accommodate count data with within-cluster correlation Understand the phenomenon of overdispersion with count data and how it can be accommodated in estimation methods |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
Course guidelines
School of Public Health (SPH) Guidelines for late submission of progressive assessment - Preamble
To apply for an extension to the due date for a piece of progressive assessment (eg assignments, oral presentations and computer-based assignments) students should complete the online request at https://my.uq.edu.au/node/218/1
Information regarding deferral of in-semester exams and end-of-semester exams is available from https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/deferring-exam
If requesting an extension on medical grounds, a medical certificate must be provided. The extension will be approved for the number of days included in the medical certificate that the student was not fit to study or work, eg if the medical certificate is for 3 days, an extension will be approved for 3 days maximum regardless of the student's request.
If requesting an extension using a Student Access Plan for Disability (SAPD) as evidence, a maximum of 7-day extension will be approved in the first instance. Updated medical documentation, as well as a copy of the SAPD, is required if requesting an extension for more than 7 days.
The maximum time for an in-semester extension is four weeks.
The following SPH guidelines are consistent with the UQ policy. However, the SPH Guidelines contain specific rules and interpretations for SPH courses, and requests for extension and penalties for late submissions will be judged according to the guidelines outlined in this document. You should read both the information in your my.UQ at the following link: https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension?p=1#1 and the SPH guidelines (below) before submitting a request for an extension. The SPH Guidelines apply to all courses offered by the School of Public Health unless the ECP explicitly states otherwise.
SPH Guidelines for late submission of progressive assessment
Initial extension for an individual item of assessment – the SPH Teaching & Assessment Support Team and/or the Course Coordinator decides.
This could be for medical or compassionate reasons, or if, in the opinion of the Course Coordinator, there are exceptional circumstances.
Acceptable and unacceptable reasons for an extension are listed at the following link, along with the required evidence to be provided: https://my.uq.edu.au/information-and-services/manage-my-program/exams-and-assessment/applying-assessment-extension?p=1#1
All requests should be lodged at least 24 hours prior to the due date for the assessment.
If applying for an extension after the due date and time of the assessment item, your request may not be approved. An explanation as to why your request was not submitted prior must be included.
If approved, a new due date will be set. This would generally be no later than 7 days after the original due date, however this can be modified to take account of the circumstances of the request and the time that would have been lost from studies.
If the new due date is past the date for submission of end-of-semester results, the student will receive an INC (incomplete) result.
Second and all subsequent extensions for an individual item of assessment – the SPH Teaching & Assessment Support Team and/or the Program Director together with the Course Coordinator decides.
This would only be approved for exceptional circumstance with supporting documentation.
- Online requests must be made at least 24 hours prior to the due date from the first extension.
- The SPH Teaching & Assessment Support Team and/or the Course Coordinator will consult with the Program Director, who will make the final decision.
- If approved, the new due date would generally be no later than 7 days after the first extension due date.
- The Program Director should consider if remedial or other support should be offered to the student.
- The Program Director should provide a report on these matters as needed at SPH Examiners’ Meetings.
Please Note: In order to support course progression, extensions that total more than 14 calendar days from the original due date of an assessment item will only be approved in very exceptional circumstances. These requests are assessed and approved or denied on a case-by-case basis.
If you have been ill or unable to attend class for more than 14 days, we advise you to carefully consider whether you are capable of successfully completing your courses this semester. You might be eligible to withdraw without academic penalty.
Penalty for late submission
Submission of assignments, practical reports, workbooks, and other types of written assessments after the due date specified in the Electronic Course Profile (ECP) will receive a penalty.
The penalty will be a deduction of 10% RELATIVE PERCENTAGE per day (24 hour period or part thereof, including weekends and public holidays) or for work graded on a 1-7 scale, a deduction of one grade per day, e.g If the original mark is 73%, then 10% relative percentage is 10% of this value, ie 7.3%, The final mark for this assessment item after applying the penalty for 1 day late submission would be 73 -7.3 = 65.7% The same outcome is achieved by multiplying the original score by .9; ie 73 x .9 = 65.7%
The penalty for multiple days late is the relative percentage multiplied by the number of days late.
A submission that is not made within 10 days of the due date will receive a mark of 0% for that assessment item.
Where a student has sought more than one extension, the due date for calculating the penalty will be the due date for the most recently approved extension.
Submission of Medical Certificates
Students are responsible for ensuring that any medical documentation they submit is authentic and signed by a registered medical practitioner. Such practitioners can be identified via the AHPRA website. Also note that:
- Not all online medical services are staffed by registered practitioners
- If the registration status of the practitioner cannot be verified, then an alternative practitioner should be sought
- Students will be held fully responsible for all documentation they submit, even if done so in ignorance of the practitioner's registration status
Medical documentation may be subjected to an audit by the University.
SPH Assessment Guidelines
The School of Public Health assessment tasks have been designed to be challenging, authentic and complex. While students may us AI technologies, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence will provide only limited support and guidance.
A failure to reference AI use may constitute student misconduct under the Student Code of Conduct.
To pass this assessment, students will be required to demonstrate detailed comprehension of their written submission independent of AI tools.