Survival Analysis with Large Dimensional Covariates: An Application in Microarray Studies


Autoria(s): Engler, David A.; Li, Yi
Data(s)

10/07/2007

Resumo

Use of microarray technology often leads to high-dimensional and low- sample size data settings. Over the past several years, a variety of novel approaches have been proposed for variable selection in this context. However, only a small number of these have been adapted for time-to-event data where censoring is present. Among standard variable selection methods shown both to have good predictive accuracy and to be computationally efficient is the elastic net penalization approach. In this paper, adaptation of the elastic net approach is presented for variable selection both under the Cox proportional hazards model and under an accelerated failure time (AFT) model. Assessment of the two methods is conducted through simulation studies and through analysis of microarray data obtained from a set of patients with diffuse large B-cell lymphoma where time to survival is of interest. The approaches are shown to match or exceed the predictive performance of a Cox-based and an AFT-based variable selection method. The methods are moreover shown to be much more computationally efficient than their respective Cox- and AFT- based counterparts.

Formato

application/pdf

Identificador

http://biostats.bepress.com/harvardbiostat/paper68

http://biostats.bepress.com/cgi/viewcontent.cgi?article=1074&context=harvardbiostat

Publicador

Collection of Biostatistics Research Archive

Fonte

Harvard University Biostatistics Working Paper Series

Palavras-Chave #Genetics #Bioinformatics #Biostatistics #Computational Biology #Genetics #Microarrays #Numerical Analysis and Computation #Statistical Methodology #Statistical Theory #Survival Analysis
Tipo

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