Predicting survival from microarray data - a comparative study
By H. Bøvelstad, S. Nygård, H. L. Størvold, M. Aldrin, Ørnulf Borgan, Arnoldo Frigessi, O. Lingjærde. Bioinform., 2007
304 citations recorded by the source corpus.
MOTIVATION Survival prediction from gene expression data and other high-dimensional genomic data has been subject to much research during the last years. These kinds of data are associated with the methodological problem of having many more gene expression values than individuals. In addition, the responses are censored survival times. Most of the proposed methods handle this by using Cox's proportional hazards model and obtain parameter estimates by some dimension reduction or parameter shrinkage estimation technique. Using three well-known microarray gene expression data sets, we compare the prediction performance of seven such methods: univariate selection, forward stepwise selection, principal components regression (PCR), supervised principal components regression, partial least squares regression (PLS), ridge regression and the lasso. RESULTS Statistical learning from subsets should be repeated several times in order to get a fair comparison between methods. Methods using coefficient shrinkage or linear combinations of the gene expression values have much better performance than the simple variable selection methods. For our data sets, ridge regression has the overall best performance. AVAILABILITY Matlab and R code for the prediction methods are available at http://www.med.uio.no/imb/stat/bmms/software/microsurv/.
Subfields: Cancer Research, Molecular Biology, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/62b9f2aa45be15ba72bbb441bd7c36f70be6249e
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Citations in the corpus
- Covariate Selection for the Semiparametric Additive Risk Model (2009, methodology): “A PLS estimator in the Cox model setting was suggested by Nygård et al. (2008) and Bøvelstad et al. (2007) studied the performance of this PLS procedure and various other methods in the Cox model setting.”
- L1 Penalized Estimation in the Cox Proportional Hazards Model (2009, background): “conclusion was reached by Bøvelstad et al. (2007) in an excellent review of survival prediction methods on several breast cancer gene expression data sets. They showed an overall best predictive performance of ridge regression, but a best performance of the lasso among prediction methods with a vari”