Simultaneous variable selection and estimation for multivariate multilevel longitudinal data with both continuous and binary responses

Complex structured data settings are studied where outcomes are multivariate and multilevel and are collected longitudinally. Multivariate outcomes include both continuous and discrete responses. In addition, the data contain a large number of covariates but only some of them are important in explaining the dynamic features of the responses. To delineate the complex association structures of the responses, a model with correlated random effects is proposed. To handle the large dimensionality of covariates, a simultaneous variable selection and parameter estimation method is developed. To implement the method, a computationally feasible algorithm is described. The proposed method is evaluated empirically by simulation studies and illustrated by analyzing the data arising from the Waterloo Smoking Prevention Project.