Daniel Meddings

Mixtures of linear mixed effects models

Linear mixed effects models are well suited to modelling clustered data, for example repeated measurements on people over time. In such data the clusters often exhibit large differences in both mean levels of response, and the relationship between the response and time. For some of these data the assumption that the data follow a single normal distribution may be not hold and instead the distribution may be a finite mixture of normal distributions. For example in clinical trials there may be two distinct sub-populations that can be classed as responders or non-responders. In these circumstances mixtures of linear mixed effects models may offer advantages over a standard linear mixed effect model. My research will focus upon some theoretical/methodological aspects of these models, as well as searching for evidence of mixture distributions in datasets, in particular for repeated measurements data from clinical trials.