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Advanced Elements of Analytics (MAST90134)
Graduate courseworkPoints: 12.5Not available in 2025
About this subject
Overview
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This subject equips students with the practical skills to apply regression methods to health data using the statistical packages R and Stata, as well as a major emphasis on the interpretation and communication of results. Topics covered include: analysis of continuous outcomes with linear regression; analysis of binary outcomes with logistic and tree-based regression methods; analysis of time-to-event outcomes with Cox and Poisson regression; fitting the aforementioned regression models in the statistical packages R and Stata; interpretation of the different measures of association estimated in each of the regression models; how to adjust for confounding and identify variables that modify measures of association using these regression methods; and purpose of regression modelling (causal vs. predictive).
Intended learning outcomes
- Demonstrate practical skills when fitting regression models to data using statistical computing software (R and/or Stata)
- Assess the suitability of a regression model with attention to checking the underlying assumptions
- Describe and demonstrate how to adjust for confounding and identify variables that modify measures of association using these regression methods
- Demonstrate the ability to interpret and effectively communicate (including visually) results of regression modelling
Generic skills
- Independent problem solving
- Facility with abstract reasoning
- Clarity of written expression
- Sound communication of technical concepts
Last updated: 8 November 2024