Partial logistic relevance vector machines in survival analysis

Nicola Lama, Patrizia Boracchi, Elia Biganzoli

Research output: Contribution to journalArticlepeer-review


The use of relevance vector machines to flexibly model hazard rate functions is explored. This technique is adapted to survival analysis problems through the partial logistic approach. The method exploits the Bayesian automatic relevance determination procedure to obtain sparse solutions and it incorporates the flexibility of kernel-based models. Example results are presented on literature data from a head-and- neck cancer survival study using Gaussian and spline kernels. Sensitivity analysis is conducted to assess the influence of hyperprior distribution parameters. The proposed method is then contrasted with other flexible hazard regression methods, in particular the HARE model proposed by Kooperberg et al. [16]. A simulation study is conducted to carry out the comparison. The model developed in this paper exhibited good performance in the prediction of hazard rate. The application of this sparse Bayesian technique to a real cancer data set demonstrated that the proposed method can potentially reveal characteristics of the hazards, associated with the dynamics of the studied diseases, which may be missed by existing modeling approaches based on different perspectives on the bias vs. variance balance.

Original languageEnglish
Pages (from-to)2445-2458
Number of pages14
JournalJournal of Applied Statistics
Issue number11
Publication statusPublished - Nov 2011


  • Automatic relevance determination
  • Bayesian methods
  • Hazard regression
  • Kernel methods
  • Relevance vector machines
  • Survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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