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 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.
van Houwelingen
Hans C
HC
Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, P.O. Box 9604, 2300 RC Leiden, The Netherlands. jcvanhouwelingen@lumc.nl
Bruinsma
Tako
T
Hart
Augustinus A M
AA
Van't Veer
Laura J
LJ
Wessels
Lodewyk F A
LF
D001943:Breast Neoplasms / Q000235:genetics; D002277:Carcinoma / Q000235:genetics; D003627:Data Interpretation, Statistical; D005260:Female; D015870:Gene Expression; D006801:Humans; D053208:Kaplan-Meier Estimate; D008875:Middle Aged; D009426:Netherlands; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models