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

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "A. Taktak and L. Antolini and M. Aung and 
                 P. Boracchi and I. Campbell and B. Damato and E. Ifeachor and 
                 N. Lama and P. Lisboa and C. Setzkorn and 
                 V. Stalbovskaya and E. Biganzoli",
  title =        "Double-blind evaluation and benchmarking of survival
                 models in a multi-centre study",
  journal =      "Computers in Biology and Medicine",
  volume =       "37",
  number =       "8",
  pages =        "1108--1120",
  year =         "2007",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Evaluation
                 studies, Double-blind study, Multi-centre studies,
                 Survival analysis, Uveal neoplasms",
  ISSN =         "0010-4825",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1016/j.compbiomed.2006.10.001",
  size =         "13 pages",
  abstract =     "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 generalisation 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 = 3
                 and 5 years. At = 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.",
  notes =        "Some confusion about 3rd author. PDF says _M_

                 Also known as \cite{Taktak20071108}",

Genetic Programming entries for Azzam F G Taktak L Antolini M Aung P Boracchi I Campbell Bertil E Damato E Ifeachor N Lama P Lisboa Christian Setzkorn V Stalbovskaya E Biganzoli