Advanced Statistical Modelling (Part-time)

Module code: MD7468

Generalised Linear Models

This module will start by introducing the theory of Generalized Linear Models (GLMs) in terms of exponential family of distributions and discussing special cases of GLMs, such as normal, Poisson or binomial regression. This will cover identifying elements of GLMs including the canonical and dispersion parameters and the mean and variance and defining the linear predictor, offset and link functions. Selection and checking of a model for a given clinical problem will be discussed in lectures followed by fitting models and running checks for a range of examples using software in computer practical classes. Further extensions will include log-linear models for multinomial distributions, over-dispersion, quasi-likelihood.

Multilevel Modelling

You'll be introduced to multilevel modelling for the analysis of hierarchical and repeated measures data for both continuous and binary outcomes. You'll have practical experience of working with a range of software packages and will also critique articles published in the medical literature using these techniques.

Introduction to multilevel (hierarchical) data structures
Summary methods
Introduction to multilevel modelling and MLwiN
Multilevel models for longitudinal/repeated measures data
Multilevel models using Stata
Repeated binary data analysis using MLwiN
Alternative analysis: Generalised Estimating Equations (GEEs)