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Large‐scale parametric survival analysis
Author(s) -
Mittal Sushil,
Madigan David,
Cheng Jerry Q.,
Burd Randall S.
Publication year - 2013
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5817
Subject(s) - overfitting , computer science , parametric statistics , range (aeronautics) , scale (ratio) , parametric model , computation , data mining , calibration , statistics , machine learning , mathematics , algorithm , materials science , artificial neural network , composite material , physics , quantum mechanics
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.