Advanced Regression (MAST90099)

Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)

You’re viewing the 2025 Handbook:
Or view archived Handbooks

Overview

Availability
September - Dual-Delivery
FeesLook up fees

This subject extends the use of regression methods in biostatistics to include the analysis of frequency counts and event rates. Generalised Linear Models (GLMs) are proposed as tools for description, prediction and causal inference in health research. Students will learn how to propose, fit and interpret Poisson regression models for counts and rates, and Cox’s proportional hazards model for time-to-event data with right censoring. The Kaplan-Meier and Mantel-Cox estimators and the log-rank test for the survival function given lifetime data will be discussed alongside regression methods for the analysis of survival data. Mathematical concepts covered include maximum likelihood estimation, the likelihood ratio test for model comparison and how GLMs provide a unifying theory for the analysis of the frequency of events occurring over time using logistic, Poisson and Cox regression.

Intended learning outcomes

On completion of this subject, students should be able to:

  • Explain the concepts of generalised linear models (GLMs) for the analysis of counts and rates as tools for description, prediction and causal inference in health research, including the required assumptions.
  • Interpret the parameters of regression models for count data and rates (Poisson regression) and time-to-event data (Cox regression).
  • Apply regression methods for count and time-to-event data to developing and validating prediction models.
  • Use multivariable regression models to estimate causal effects such as the rate ratio or hazard ratio, based on standard epidemiological study designs, including the required causal and parametric assumptions.
  • Implement the life table and Kaplan-Meier procedures for estimating survival curves both manually and with the use of a computer.
  • Use statistical software to fit regression models to data with outcomes presented as counts, rates and time-to-event data.

Generic skills

  • Independent problem solving,
  • Facility with abstract reasoning,
  • Clarity of written expression,
  • Sound communication of technical concepts

Last updated: 4 March 2025

Eligibility and requirements

Prerequisites

All of

Code Name Teaching period Credit Points
MAST90100 Probability & Inference in Biostatistics
Semester 1 (Dual-Delivery - Parkville)
 12.5
MAST90102 Foundations of Regression
July (Dual-Delivery - Parkville)
 12.5
POPH90014 Epidemiology 1
Semester 2 (Dual-Delivery - Parkville)
Semester 1 (Dual-Delivery - Parkville)
 12.5

Corequisites

None

Non-allowed subjects

None

Inherent requirements (core participation requirements)

The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.

Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home

Last updated: 4 March 2025

Assessment

DescriptionTimingPercentage

Practical exercise 1

  • 1000 words
Week 320%

Major assignment 1

  • 2000 words
Week 540%

Major assignment 2

  • 2000 words
2 Weeks after the end of teaching40%

Last updated: 4 March 2025

Dates & times

  • September
    CoordinatorLyle Gurrin
    Mode of deliveryDual-Delivery (Parkville)
    Contact hours
    Total time commitment170 hours
    Teaching period 8 September 2025 to 26 October 2025
    Last self-enrol date17 September 2025
    Census date29 September 2025
    Last date to withdraw without fail17 October 2025
    Assessment period ends 7 November 2025

    September contact information

    lgurrin@unimelb.edu.au

    Melbourne School of Population and Global Health

    OR

    Currently enrolled students:

    Future Students:



Time commitment details

170 hours

Last updated: 4 March 2025

Further information

  • Texts

    Prescribed texts

    Resources Provided to Students online: Course notes and assignments.
    Special Computer Requirements: Stata and R (open access) statistical software.

  • Related Handbook entries

    This subject contributes to the following:

    TypeName
    CourseMaster of Biostatistics
    CourseGraduate Diploma in Biostatistics
  • Available through the Community Access Program

    About the Community Access Program (CAP)

    This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.

    Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants

    Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.

  • Available to Study Abroad and/or Study Exchange Students

    This subject is available to students studying at the University from eligible overseas institutions on exchange and study abroad. Students are required to satisfy any listed requirements, such as pre- and co-requisites, for enrolment in the subject.

Last updated: 4 March 2025