Double-blind evaluation and benchmarking of survival models in a multi-centre study

A. Taktak, L. Antolini, M. Aung, P. Boracchi, I. Campbell, B. Damato, E. Ifeachor, N. Lama, P. Lisboa, C. Setzkorn, V. Stalbovskaya, E. Biganzoli

Research output: Contribution to journalArticlepeer-review


Accurate modelling of time-to-event data is of particular importance for both exploratory and predictive analysis in cancer, and can have a direct impact on clinical care. This study presents a detailed double-blind evaluation of the accuracy in out-of-sample prediction of mortality from two generic non-linear models, using artificial neural networks benchmarked against a partial logistic spline, log-normal and COX regression models. A data set containing 2880 samples was shared over the Internet using a purpose-built secure environment called GEOCONDA ( The evaluation was carried out in three parts. The first was a comparison between the predicted survival estimates for each of the four survival groups defined by the TNM staging system, against the empirical estimates derived by the Kaplan-Meier method. The second approach focused on the accurate prediction of survival over time, quantified with the time dependent C index (Ctd). Finally, calibration plots were obtained over the range of follow-up and tested using a generalization of the Hosmer-Lemeshow test. All models showed satisfactory performance, with values of Ctd of about 0.7. None of the models showed a systematic tendency towards over/under estimation of the observed survival at τ = 3 and 5 years. At τ = 10 years, all models underestimated the observed survival, except for COX regression which returned an overestimate. The study presents a robust and unbiased benchmarking methodology using a bespoke web facility. It was concluded that powerful, recent flexible modelling algorithms show a comparative predictive performance to that of more established methods from the medical and biological literature, for the reference data set.

Original languageEnglish
Pages (from-to)1108-1120
Number of pages13
JournalComputers in Biology and Medicine
Issue number8
Publication statusPublished - Aug 2007


  • Double-blind study
  • Evaluation studies
  • Multi-centre studies
  • Survival analysis
  • Uveal neoplasms

ASJC Scopus subject areas

  • Computer Science Applications


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