EPM5009 - Categorical data and generalised linear models - 2017

This unit will explore biostatistical applications of generalised linear models with an emphasis on underlying theoretical issues, and practical interpretation of the results of fitting these models. Relevant methods for 2 x 2 and 2 x k tables extended into logistic regression for a binary outcome as a special case of generalised linear modelling. Measures of association and modelling techniques for ordinal outcomes. Methods for analysing count data. Techniques for dealing with matched data e.g. from case control studies.

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

1. Understand the major theoretical aspects of generalised linear models.
2. Appreciate regression modelling strategies for generalised linear models.
3. Including estimation issues, choice of models, prediction and goodness of fit of a selected model.
4. Be proficient in the analysis of binary outcome data, either form a standard study design or from a matched study design.
5. Be capable of analysing ordered and unordered categorical outcomes using simple measures of association and complex regression models.
6. Be capable of analysing count data whether it satisfies standard distributional assumptions or whether it is over dispersed.

• 3 x Written assignments (35%, 35%, 30%)

Professor Andrew Forbes

Biostatistics

EPM5002, EPM5003, EPM5014, MPH5040.

EPM5004.

This unit is only available to students enrolled in the Graduate Certificate, Graduate Diploma or Masters of Biostatistics.

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