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Fitting survival models with P>>n predictors: beyond proportional hazards

Tuesday 24th June 2008 - 09:00 to 10:00
INI Seminar Room 1
Session Chair: 
Peter Hall

In a recent paper by Bovelstad et al. [1] partial likelihood ridge regression as used in [2] 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 [2] 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 [3] 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 [1] Bovelstad, HM; Nygard, S; Storvold, HL; et al. Predicting survival from microarray data - a comparative study BIOINFORMATICS, 23 (16): 2080-2087 AUG 15 2007 [2] 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 [3] 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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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons