Convex Optimization
Predicting survival from microarray data—a comparative study
Bioinformatics
Sparse kernel methods for high-dimensional survival data
Bioinformatics
A Support Vector Approach to Censored Targets
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Support Vector Regression for Censored Data (SVRc): A Novel Tool for Survival Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Objective: To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. Methods: The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. Results: We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. Conclusions: This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods including only regression or both regression and ranking constraints on clinical data. On high dimensional data, the former model performs better. However, this approach does not have a theoretical link with standard statistical models for survival data. This link can be made by means of transformation models when ranking constraints are included.