Large-scale parametric survival analysis

Authors


Correspondence to: Sushil Mittal, Department of Statistics, Columbia University, 1255 Amsterdam Avenue, New York, NY 10027, U.S.A.

E-mail: mittal@stat.columbia.edu

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. Copyright © 2013 John Wiley & Sons, Ltd.

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