2 citations · 5 across the 12 of their papers we have counts for
12 papers
Bayesian Causal Machine Learning for Cure Models
Antonio R. Linero, F. Javier Rubio, Piyali Basak
In survival studies, treatments can benefit patients through different mechanisms: a treatment may increase the probability of being cured or delay failure among patients who are n…
Conformalized Super Learner
Zhanli Wu, Fabrizio Leisen, Miguel-Angel Luque-Fernandez +1
The Super Learner (SL) is a widely used ensemble method that combines point predictions from a library of learners based on their predictive performance. Interval predictions are o…
Relative Survival Analysis Using Bayesian Decision Tree Ensembles
Piyali Basak, Antonio R. Linero, Camille Maringe +1
In cancer epidemiology, the \emph{relative survival framework} is used to quantify the hazard associated with cancer by comparing the all-cause mortality hazard in cancer patients…
On harmonic oscillator hazard functions
J. A. Christen, F. J. Rubio
We propose a parametric hazard model obtained by enforcing positivity in the damped harmonic oscillator. The resulting model has closed-form hazard and cumulative hazard functions,…
Dynamic survival analysis: modelling the hazard function via ordinary differential equations
J. A. Christen, F. J. Rubio
The hazard function represents one of the main quantities of interest in the analysis of survival data. We propose a general approach for parametrically modelling the dynamics of t…
Extended Excess Hazard Models for Spatially Dependent Survival Data
André Victor Ribeiro Amaral, Francisco Javier Rubio, Manuela Quaresma +2
Relative survival represents the preferred framework for the analysis of population cancer survival data. The aim is to model the survival probability associated to cancer in the a…
Individual frailty excess hazard models in cancer epidemiology
F. J. Rubio, H. Putter, A. Belot
Unobserved individual heterogeneity is a common challenge in population cancer survival studies. This heterogeneity is usually associated with the combination of model misspecifica…
MEGH: A parametric class of general hazard models for clustered survival data
Rubio, F. J., Drikvandi +1
In many applications of survival data analysis, the individuals are treated in different medical centres or belong to different clusters defined by geographical or administrative r…
A Unifying Framework for Flexible Excess Hazard Modeling with Applications in Cancer Epidemiology
A. Eletti, G. Marra, M. Quaresma +2
Excess hazard modeling is one of the main tools in population-based cancer survival research. Indeed, this setting allows for direct modeling of the survival due to cancer even in…
On a prior based on the Wasserstein information matrix
W. Li, F. J. Rubio
We introduce a prior for the parameters of univariate continuous distributions, based on the Wasserstein information matrix, which is invariant under reparameterisations. We discus…
On models for the estimation of the excess mortality hazard in case of insufficiently stratified life tables
Francisco J. Rubio, Bernard Rachet, Roch Giorgi +2
In cancer epidemiology using population-based data, regression models for the excess mortality hazard is a useful method to estimate cancer survival and to describe the association…
On a general structure for hazard-based regression models: an application to population-based cancer research
Francisco J. Rubio, Laurent Remontet, Nicholas P. Jewell +1
The proportional hazards model represents the most commonly assumed hazard structure when analysing time to event data using regression models. We study a general hazard structure…