Multiple endpoints of different kinds (discrete, continuous, count, ordinal) are ubiquitous in biomedical and biopharmaceutical studies. This is usually because a single endpoint is not adequate to describe the disease complexities and associations among these multiple endpoints are important in characterizing the treatment effectiveness. There are usually several levels of correlations inherent in such mixed outcomes, especially when dealing with a clustered or longitudinal design and procedures which analyze each outcome separately ignore these correlations and present misleading results. In this presentation, we introduce spike-and-slab LASSO with mixed endpoints (SSLME) to guide the estimation and efficient extraction of active predictors in longitudinal studies with high-dimensional covariates. We illustrate our methodology through extensive simulations analysis of real data.