Time-to-event latent variable models for the statistical analysis of clinical data

In clinical research, interest sometimes lies in analysing variables which are not measured directly. Instead, information about these ‘latent variables’ can be inferred from surrogates or other imperfect indicators, using latent variable models. Common examples of ‘hypothetical’ latent variables in clinical research include quality of life (QoL), anxiety and depression. Another type of latent variable is a variable used as a device for dimension reduction, for example, a principal component. The aim of this thesis is to explore and develop latent variable methods for the statistical analysis of clinical data, with an emphasis on including latent variables in time-to-event models.