Regularized Weighted Linear Regression for High-dimensional Censored Data

Survival analysis aims at modeling time to event data which occurs ubiquitously in many biomedical and healthcare applications. One of the critical challenges with modeling such survival data is the presence of censored outcomes which cannot be handled by standard regression models. In this paper, we propose a regularized linear regression model with weighted least-squares to handle the survival prediction in the presence of censored instances. We also employ the elastic net penalty term for inducing sparsity into the linear model for effectively handling high-dimensional data. As opposed to the existing censored linear models, the parameter estimation of our model does not need any prior estimation of survival times of censored instances. In addition, we propose a self-training framework which is able to improve the prediction performance of our proposed linear model. We demonstrate the performance of the proposed model using several real-world high-dimensional biomedical benchmark datasets and our experimental results indicate that our model outperforms other related competing methods and attains very competitive performance on different datasets.