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Advanced Regression (MAST90099)
Graduate courseworkPoints: 12.5Dual-Delivery (Parkville)
About this subject
Contact information
September
Melbourne School of Population and Global Health
OR
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Email: Contact Stop 1
Future Students:
- Further Information: https://study.unimelb.edu.au/
Overview
| Availability | September - Dual-Delivery |
|---|---|
| Fees | Look 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: 8 November 2024