Cross-validated Cox regression on microarray gene expression data

This paper describes how penalized Cox regression, in combination with cross‐validated partial likelihood can be employed to obtain reliable survival prediction models for high dimensional microarray data. The suggested procedure is demonstrated on a breast cancer survival data set consisting of 295...

Full description

Saved in:
Bibliographic Details
Published in:Statistics in medicine 2006-09, Vol.25 (18), p.3201-3216
Main Authors: van Houwelingen, Hans C., Bruinsma, Tako, Hart, Augustinus A. M., van't Veer, Laura J., Wessels, Lodewyk F. A.
Format: Article
Language:English
Subjects:
Breast cancer
Breast Neoplasms - genetics
Carcinoma - genetics
Cox regression
cross-validation
Data Interpretation, Statistical
Female
Gene Expression
gene expression data
high-dimensional data
Humans
Kaplan-Meier Estimate
Middle Aged
Netherlands
Oligonucleotide Array Sequence Analysis - methods
Predictive Value of Tests
Proportional Hazards Models
Regression analysis
Tumors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper describes how penalized Cox regression, in combination with cross‐validated partial likelihood can be employed to obtain reliable survival prediction models for high dimensional microarray data. The suggested procedure is demonstrated on a breast cancer survival data set consisting of 295 tumours as collected in the National Cancer Institute in Amsterdam and previously reported in more general papers. The main aim of this paper it to show how generally accepted biostatistical procedures can be employed to analyse high‐dimensional data. Copyright © 2005 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.2353