Survival Analysis on Rare Events Using Group-Regularized Multi-Response Cox Regression
MOTIVATION: The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data. RESULTS: We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events.
MOTIVATION: The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data.
RESULTS: We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al.
AVAILABILITYANDIMPLEMENTATION: https://github.com/rivas-lab/multisnpnet-Cox.
In this paper led by Ruilin Li, we describe a new method to fit a sparse Cox Model for multiple time-to-event phenotypes from a large-scale (> 1 million features) genetic dataset.
