A Change-Point Model for Detecting Heterogeneity in Ordered Survival Responses

Abstract : In this article we suggest a new statistical approach considering survival heterogeneity as a breakpoint model in an ordered sequence of time to event variables. The survival responses need to be ordered according to a numerical covariate. Our esti- mation method will aim at detecting heterogeneity that could arise through the or- dering covariate. We formally introduce our model as a constrained Hidden Markov Model (HMM) where the hidden states are the unknown segmentation (breakpoint locations) and the observed states are the survival responses. We derive an efficient Expectation-Maximization (EM) framework for maximizing the likelihood of this model for a wide range of baseline hazard forms (parametrics or nonparametric). The posterior distribution of the breakpoints is also derived and the selection of the number of segments using penalized likelihood criterion is discussed. The performance of our survival breakpoint model is finally illustrated on a diabetes dataset where the observed survival times are ordered according to the calendar time of disease onset.
Type de document :
Pré-publication, Document de travail
Liste complète des métadonnées

Contributeur : Olivier Bouaziz <>
Soumis le : vendredi 23 septembre 2016 - 10:35:32
Dernière modification le : mardi 10 octobre 2017 - 13:46:16


  • HAL Id : hal-01287075, version 2
  • ARXIV : 1603.04189



Olivier Bouaziz, Grégory Nuel. A Change-Point Model for Detecting Heterogeneity in Ordered Survival Responses. 2016. 〈hal-01287075v2〉



Consultations de la notice


Téléchargements de fichiers