title: Flexible modeling of malignant melanoma survival.
abstract: OBJECTIVE
  This paper compares the diagnostic capabilities of flexible ensemble methods modeling the survival time of melanoma patients in comparison to the well established proportional hazards model. Both a random forest type algorithm for censored data as well as a model combination of the proportional hazards model with recursive partitioning are investigated.
  
  
  METHODS
  Benchmark experiments utilizing the integrated Brier score as a measure for goodness of prediction are the basis of the performance assessment for all competing algorithms. For the purpose of comparing regression relationships represented by the models under test, we describe fitted conditional survival functions by a univariate measure derived from the area under the curve. Based on this measure, we adapt a visualization technique useful for the inspection and comparison of model fits.
  
  
  RESULTS
  For the data of malignant melanoma patients the predictive performance of the competing models is on par, allowing for a fair comparison of the fitted relationships. Newly introduced MODplots visualize differences in the fitting structure of the underlying models.
  
  
  CONCLUSIONS
  The paper provides a framework for comparing the predictive and diagnostic performance of a parametric, a non-parametric and a combined approach.
authors.affiliation: Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Waldstr. 6, 91054 Erlangen, Germany.
authors.forename: K T
authors.initials: KT
authors.lastname: Eckel
authors.forename: A
authors.initials: A
authors.lastname: Pfahlberg
authors.forename: O
authors.initials: O
authors.lastname: Gefeller
authors.forename: T
authors.initials: T
authors.lastname: Hothorn
mesh_terms: D000328:Adult; D000465:Algorithms; D019985:Benchmarking; D005260:Female; D006801:Humans; D008297:Male; D008545:Melanoma / Q000401:mortality*; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D011379:Prognosis; D016016:Proportional Hazards Models