Louise Marquart-Wilson

Generalised linear mixed effects models

Research focusing on the application, extension and evaluating statistical performance of generalised mixed effects models (GLMMs) within medical and public health applications. GLMMs are a flexible statistical methodology to account for hierarchical structure that are inherent in some datasets. GLMMs can be used for the analysis of longitudinal data, and can be utilised when there is additional hierarchical structure (i.e. repeated measures of people nested within clusters). There are four topics within this theme which include: 1. Investigating the impact of violating assumptions of GLMMs, 2. linear and non-linear mixed effects models for malaria parasite dynamics, 3. segmented mixed effects models (interrupted time series), and 4. analysis of repeated measures patient reported outcome measures.