Analysis of additive risk model with high-dimensional covariates using partial least squares.

Abstract:

:In this paper, we construct a partial additive regression (PAR) model to predict the survival times of cancer patients based on microarray gene expression data with right censoring. The area under time-dependent receiver operating characteristic curve is used as a model evaluation criterion. We conduct a simulation study to compare the proposed method with other methods, i.e. partial Cox regression and supervised principal component analysis. Two data sets of breast cancer and diffuse large B-cell lymphoma are analyzed to illustrate our procedure. The outcome indicates great predictive performance on both dimension reduction and predictive performance of the proposed PAR model.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Zhao Y,Zhou Y,Zhao M

doi

10.1002/sim.3412

subject

Has Abstract

pub_date

2009-01-30 00:00:00

pages

181-93

issue

2

eissn

0277-6715

issn

1097-0258

journal_volume

28

pub_type

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