Analysis of Melanoma Data with a Mixture of Survival Models Utilising Multi-class DLDA to Inform Mixture Class

Melanoma is a prevalent skin cancer in Australia, with close to 14000 new cases estimated to be diagnosed in 2017. Survival times are markedly different from one individual to the next. In particular, there appears to be three classes of survival outcome. This talk considers integrating survival time data with microarray gene expression data. We construct a hybrid model that seamlessly integrates a three-class linear discriminant analysis model, mixture of parametric survival models, and model selection components. We fit this model using a variational expectation maximization (VEM) approach. Our model selection component naturally simplifies as a function of likelihood ratio statistics allowing natural comparisons with traditional hypothesis testing methods. We compare our method with several naïve approaches which only addresses the classification aspect or survival model aspect in isolation