Predicting survival from microarray data is not easy due to the problem of high dimensionality of data and the existence of censored observations. Also the limitation of individual gene analysis causes the shift of focus to the level of gene sets with functionally related genes. For developing a survival prediction model based on pathway information, the methods for selecting a supergene using principal component analysis and testing its significance for each pathway are discussed. Besides, the performance of gene filtering is compared.