EPM5004 - Linear models - 2019

This unit explores biostatistical applications of linear models with an emphasis on underlying theoretical and computational issues, practical interpretation and communication of results. By a series of case studies, students explore extensions of methods for group comparisons of means (t-tests and analysis of variance) to adjust for confounding and to assess effect modification/interaction, together with the development of associated inference procedures. Multiple regression strategies and model selection issues will be presented together with model checking and diagnostics. Nonparametric regression techniques, and random effects and variance components models will also be outlined.

Upon successful completion of this unit, students should be able to:

1. Understand the major theoretical and computational issues underlying analyses based on linear models.
2. Develop appropriate regression modelling strategies based on unit matter considerations, including choice of models, control for confounding and appropriate parameterisation.
3. Be proficient at using a statistical software package (e.g. Strata) to perform multiple regression and analysis of variance.
4. Understand the construction, use and interpretation of regression modelling diagnostics.
5. Express the results of statistical analyses of linear models in language suitable for communication to medical investigators or publication in biomedical or epidemiological journal articles.
6. appreciate the role of modern techniques including non-parametric smoothing and variance components models.

• 2 x Written assignments (30% each)
• Practical exercises (40%) (Hurdle)

Biostatistics

http://www.med.monash.edu.au/epidemiology/pgrad