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Course profile

Regression Modelling for Biostatistics 2 (STAT7619)

Study period
Sem 2 2025
Location
External
Attendance mode
Online

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

Dr Michael Waller

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
  • Online
30%

14/04/2025 2:00 pm

Refer to Study Guide

Paper/ Report/ Annotation Assessment 2, covering Module 3
  • Online
30%

12/05/2025 2:00 pm

Refer to Study Guide

Paper/ Report/ Annotation Assignment 3, covering Modules 1, 2, 3, 4 & 5
  • Online
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.

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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:

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.