Large-scale parametric survival analysis
Statistics in Medicine
Parametric models; Pediatric trauma; Penalized regression; Regularization; Survival analysis
Survival analysis has been a topic of active statistical research in the past few decades with applications spread across several areas. Traditional applications usually consider data with only a small numbers of predictors with a few hundreds or thousands of observations. Recent advances in data acquisition techniques and computation power have led to considerable interest in analyzing very-high-dimensional data where the number of predictor variables and the number of observations range between 10 4 and 10 6. In this paper, we present a tool for performing large-scale regularized parametric survival analysis using a variant of the cyclic coordinate descent method. Through our experiments on two real data sets, we show that application of regularized models to high-dimensional data avoids overfitting and can provide improved predictive performance and calibration over corresponding low-dimensional models. © 2013 John Wiley & Sons, Ltd.
Mittal, S., Madigan, D., Cheng, J., & Burd, R. (2013). Large-scale parametric survival analysis. Statistics in Medicine, 32 (23). http://dx.doi.org/10.1002/sim.5817