Course overview
This course aims to enable students to implement generalised 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. Specifically, this course presents the theory and application of GLMs and survival analysis. This course covers the implementation of GLMs to analyse count data using Poisson and negative binomial regression; how logistic regression models can be applied to binary, multinominal, and ordinal data; and the use of GLMs with continuous data. The course presents methods to analyse time to event survival data including the Kaplan Meier curve and the Cox proportional hazards model.
Course learning outcomes
- See Study Guides at: https://url.au.m.mimecastprotect.com/s/jyzMCmO5QMCjMl1XJtJi1URPVES?domain=bca.edu.au/
- Explain the theory of GLMs and statistical inference based on GLMs
- Analyse data using logistic regression models for binary, multinomial and ordinal categorical data
- Analyse count and rate data using Poisson regression, Negative Binomial, and continuous data using GLMs
- Explain the nature of survival data and summarise and display survival data using nonparametric methods, including the Kaplan-Meier curve
- Analyse survival data using the Cox proportional hazards model, including time dependent covariates and the stratified Cox model
- To assess and evaluate the model fit and diagnostics of GLMs and survival models
- Synthesise results of analyses to present and communicate findings