Modelling Multivariate Nonlinear Vaccine Induced Immune Responses - Lapham, Brendon M (Department of Statistical Sciences, 2020)
Interpretable statistical models for multivariate vaccine induced immune response data are important as they provide a rigorous means of deciding which vaccine candidates should be advanced in the clinical trials process. We consider applications of several different statistical models to a vaccine data set which contains multivariate immune responses for several novel Tuberculosis vaccines and the current BCG vaccine. The immune responses in the data set have several features which the models need to account for. In particular, the models need to account for the multivariate repeated measures for the subjects, the nonlinear profiles of the immune responses, and the zero-inflated skew distributions of the immune responses. We find that Tweedie multivariate generalised linear mixed effect and latent variable models with cubic B-splines perform well for this data set relative to linear, nonlinear, and univariate Tweedie generalised linear mixed effect models. In addition, the Tweedie multivariate generalised linear mixed effect and latent variable models have several advantages over the other models we consider and are also capable of interpretation; importantly, we are able to draw clinical conclusions about which novel TB vaccine candidates appear to be the most promising.