Predicting survival from gene expression data by generalized partial least squares regression

Excerpt

There is considerable interest in linking microarray-based gene expression profiles to clinical endpoint variables such as survival. Standard statistical methodologies typically fail when the number of covariates (genes) far exceeds the number of samples (patients). For example, the standard Cox proportional hazards model cannot be directly applied to data of this form. Several methods have been proposed for dealing with this problem in Cox regression, including partial least squares regression (PLS) [1]. Nguyen and Rocke [2] proposed first applying PLS in order to derive a small set of covariates, and then performing proportional hazards regression on the reduced set of covariates. In their approach, however, PLS is applied to survival times without taking into consideration the fact that the latter may be censored. A further problem with their approach is that the PLS step of their procedure is based on the assumption of a Gaussian (normal) likelihood. …