New methods for detecting and modelling heterogeneity in survival responses
schedule le mardi 27 mars 2018 de 10h45 à 11h45
Organisé par : Castillo, Fischer, Giulini, Gribkova, Levrard, Roquain, Sangnier
Intervenant : Olivier Bouaziz (Universty Paris Descartes)
Lieu : UPMC, salle 15-16.413
Sujet : New methods for detecting and modelling heterogeneity in survival responses
The first one considers survival heterogeneity as a breakpoint model in an ordered sequence of survival responses. These responses might be ordered according to any numerical covariate like the date of diagnosis. In such a model, we aim at estimating the hazard rates in each homogenous region using a Cox model and at accurately providing the number and location of the breakpoints. A constrained Hidden Markov Model (HMM) is implemented which performs a full change-point analysis in a segment-based model providing linear estimates of the parameters (using the EM algorithm) and a full specification of the posterior distribution of change points.
The second method specifically models age, period and cohort effects (with the relation period=age+cohort) through a bi-dimensional hazard estimation. Since the number of parameters can be quite large compared to the sample size, an L0 penalized likelihood method is implemented to avoid overfitting issues. The method is based on the adaptive ridge method from Frommlet and Nuel 2016. It allows to detect the homogeneous hazard rate regions in the age/period, age/cohort and cohort/period planes and can be seen as an extension of classical age-period-cohort models allowing for interactions between the three effects.