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 presents the theory and application of generalised linear models (GLMs) and survival analysis, paying proper attention to the underlying assumptions of these models. The unit covers the implementation of GLMs to analyse count data using Poisson and negative binomial regression; how logistic regression models can be applied to binary, multinomial, and ordinal data; and the use of GLMs with continuous data. The unit presents methods to analyse time to event survival data including the Kaplan Meier curve, the Cox proportional hazards model, and parametric accelerated failure time models. A major focus is on selection of appropriate methods, assessing the model fit and diagnostics of GLMs and survival models, and the practical interpretation and communication of model results.
The aim of this course is to enable students to implement generalized linear models (GLMs) for analysis of categorical data, and survival analysis methods for time-to-event data, with proper attention to the underlying assumptions. A major focus is on selection of appropriate methods, assessing the model fit and diagnostics of GLMs and survival models, and the practical interpretation and communication of model results.ᅠᅠ
Course requirements
Assumed background
This course builds on the material taught in STAT7618 Regression Modelling for Boistatistics 1 and covers generalized linear models and survival analysis techniques.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
STAT7618
Incompatible
You can't enrol in this course if you've already completed the following:
STAT7608, STAT7609
Course contact
Course staff
Lecturer
Timetable
The timetable for this course is available on the UQ Public Timetable.
Additional timetable information
Teaching Starts Monday 3rd March 2025
Aims and outcomes
This course presents the theory and application of generalised linear models (GLMs) and survival analysis, paying proper attention to the underlying assumptions of these models.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Explain the theory of generalised linear models (GLMs) and statistical inference based on GLMs
LO2.
Analyse data using logistic regression models for binary, multinomial and ordinal categorical data
LO3.
Analyse count and rate data using Poisson regression, Negative Binomial, and continuous data using GLMs
LO4.
Explain the nature of survival data, and summarise and display survival data using nonparametric methods, including the Kaplan-Meier curve
LO5.
Analyse survival data using the Cox proportional hazards regression model, including time-dependent covariates and the stratified Cox model
LO6.
To assess and evaluate the model fit and diagnostics of GLMs and survival models
LO7.
Synthesise results of analyses to present and communicate findings
Assessment
Assessment summary
Category | Assessment task | Weight | Due date |
---|---|---|---|
Paper/ Report/ Annotation |
Assessment 1, covering Modules 1 & 2
|
30% |
14/04/2025 2:00 pm
Refer to Study Guide |
Paper/ Report/ Annotation |
Assessment 2, covering Module 3
|
30% |
12/05/2025 2:00 pm
Refer to Study Guide |
Paper/ Report/ Annotation |
Assignment 3, covering Modules 1, 2, 3, 4 & 5
|
40% |
10/06/2025 2:00 pm
Refer to Study Guide |
Assessment details
Assessment 1, covering Modules 1 & 2
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 30%
- Due date
14/04/2025 2:00 pm
Refer to Study Guide
Task description
Refer to Study Guide
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
Late submission
Please see 10. Policies & Guidelines
Assessment 2, covering Module 3
- Online
- Mode
- Written
- Category
- Paper/ Report/ Annotation
- Weight
- 30%
- Due date
12/05/2025 2:00 pm
Refer to Study Guide
Task description
Refer to Study Guilde
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
Late submission
Please see 10. Policies & Guidelines
Assignment 3, covering Modules 1, 2, 3, 4 & 5
- Online
- Mode
- Oral, Written
- Category
- Paper/ Report/ Annotation
- Weight
- 40%
- Due date
10/06/2025 2:00 pm
Refer to Study Guide
Task description
Refer to Study Guide
Submission guidelines
Deferral or extension
You may be able to apply for an extension.
Please see 10. Policies & Guidelines
Late submission
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.
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.
Additional learning resources information
For this course you will need to have access to R or Stata. Code and output in the module notes are given in both R and Stata, and students may choose to work in either software language.However, we expect most of you would be using Stata 13-18. We are not aware of any major differences between Stata versions that affect the material, but minor issues will be pointed out in eLearning postings.ᅠ
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 |
Modules 1 The Exponential Family of Distribution and Generalized Linear Models. Maximum Likelihood Estimation for GLMs. Inference for GLMs, including Likelihood Ratio test, Wald statistic and the Deviance. Checking the model assumptions and assessing the goodness of fit of GLMs. Selection of distribution and choice of link function for a GLM. Refer to Study Guide |
Tutorial |
Module 2 AIC and BIC statistics. GLMs for continuous outcome data. Analysis of count and rate data using Poisson regression and Negative Binomial models. Logistic regression models for binary, multinomial and ordinal categorical data Refer to Study Guide |
|
Tutorial |
Module 3 Life tables. The nature of survival data, including censoring; the survival function: definition and estimation via the Kaplan-Meier curve; Kaplan-Meier estimate of the survival function: confidence intervals and hypothesis testing. the stset command in Stata; Surv function in R; The density, survival, hazard and cumulative hazard functions; the Nelson-Aalen estimate of the cumulative hazard function; Definition of the proportional hazards model; construction of the partial likelihood of the Cox model. Refer to Study Guide |
|
Tutorial |
Module 4 Hypothesis testing on the coefficients of the Cox model; estimation of the baseline functions S0(t) and H0(t), and their adjustment for covariate values; the effect of a change in scale and origin of units of measurement of covariates. Model diagnostics for the Cox PH model. Refer to Study Guide |
|
Tutorial |
Module 5 Time-dependent covariates in the Cox model; Stratified Cox Model. Parametric survival time models; discrete-time logistic model. Sample size for survival. Refer to Study Guide. |
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.