Fitting survival models with P>>n predictors: beyond proportional hazards
Seminar Room 1, Newton Institute
In a recent paper by Bovelstad et al.  partial likelihood ridge regression as used in  turned out to be the most successful approach to predicting survival with gene expression data.
However the proportional hazard model used in these models is quite simple and might not be realistic if there is a long survival follow-up. Exploring the fit of the model by using a cross-validated prognostic index leads to the conclusion that the effect of the predictor derived in  is neither linear nor constant over time.
We will discuss penalized reduced rank models as a way to obtain robust extensions of the Cox model for this type of data. For time varying effects the reduced rank model of  can be employed, while nonlinear effects can be introduced by means of bilinear terms. The predictive performance of such models can be regulated by penalization in combination with cross-validation.
References  Bovelstad, HM; Nygard, S; Storvold, HL; et al. Predicting survival from microarray data - a comparative study BIOINFORMATICS, 23 (16): 2080-2087 AUG 15 2007  van Houwelingen, HC; Bruinsma, T; Hart, AAM; et al. Cross-validated Cox regression on microarray gene expression data STATISTICS IN MEDICINE, 25 (18): 3201-3216 SEP 30 2006  Perperoglou, A; le Cessie, S; van Houwelingen, HC Reduced-rank hazard regression for modeling non-proportional hazards STATISTICS IN MEDICINE, 25 (16): 2831-2845 AUG 30 2006
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