title: Tree-structured prediction for censored survival data and the Cox model.
abstract: Prediction trees for the analysis of survival data are discussed. It is shown that trees are useful not only in summarizing the prognostic information contained in a set of covariates (prognostic classification), but also in detecting and displaying treatment-covariates interactions (subgroup analysis). The RECPAM approach to tree-growing is outlined; prognostic classification and subgroup analysis are then formulated within the RECPAM framework and on the basis of the Cox proportional hazards models with a priori strata. Two examples of data analysis are presented. The issue of cross-validation is discussed in relation to computationally cheaper model selection criteria.
authors.affiliation: McGill University/Montreal Childrens Hospital Research Institute, Canada.
authors.forename: A
authors.initials: A
authors.lastname: Ciampi
authors.forename: A
authors.initials: A
authors.lastname: Negassa
authors.forename: Z
authors.initials: Z
authors.lastname: Lou
mesh_terms: D000465:Algorithms; D003663:Decision Trees; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification / Q000401:mortality; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D016016:Proportional Hazards Models; D012984:Software; D016019:Survival Analysis