Bayesian variable selection and shrinkage strategies in a complicated modelling setting with missing data: A case study using multistate models

Abstract

Multistate modelling is a strategy for jointly modelling related time-to-event outcomes that can handle complicated outcome relationships, has appealing interpretations, can provide insight into different aspects of disease development and can be useful for making individualized predictions. A challenge with using multistate modelling in practice is the large number of parameters, and variable selection and shrinkage strategies are needed in order for these models to gain wider adoption. Application of existing selection and shrinkage strategies in the multistate modelling setting can be challenging due to complicated patterns of data missingness, inclusion of highly correlated predictors and hierarchical parameter relationships. In this article, we discuss how to modify and implement several existing Bayesian variable selection and shrinkage methods in a general multistate modelling setting. We compare the performance of these methods in terms of parameter estimation and model selection in a multistate cure model of recurrence and death in patients treated for head and neck cancer. We can view this work as a case study of variable selection and shrinkage in a complicated modelling setting with missing data.