title: Large-scale parametric survival analysis.
abstract: 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.
authors.affiliation: Department of Statistics, Columbia University, 1255 Amsterdam Avenue, New York, NY 10027, USA. mittal@stat.columbia.edu
authors.forename: Sushil
authors.initials: S
authors.lastname: Mittal
authors.forename: David
authors.initials: D
authors.lastname: Madigan
authors.forename: Jerry Q
authors.initials: JQ
authors.lastname: Cheng
authors.forename: Randall S
authors.initials: RS
authors.lastname: Burd
mesh_terms: D000293:Adolescent; D001943:Breast Neoplasms / Q000401:mortality; D002648:Child; D002675:Child, Preschool; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D016019:Survival Analysis; D014947:Wounds and Injuries / Q000401:mortality
keywords: parametric models; pediatric trauma; penalized regression; regularization; survival analysis