Francisco Javier Rubio

Lecturer

University College London

United Kingdom

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Francisco Javier Rubio is a Lecturer at University College London, specializing in cancer epidemiology and survival analysis. His recent research includes studies on inequalities in cancer survival, machine learning applications in healthcare, and the impact of socio-economic factors on cancer diagnosis. He has published works exploring dynamic survival models, hazard functions, and the effects of distraction on driving behavior. His contributions aim to enhance understanding of socio-territorial inequities and improve cancer control strategies.

Francisco Javier Rubio

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Articles (16)

Understanding Inequalities in Cancer Survival Using Bayesian Machine Learning

On harmonic oscillator hazard functions

An index of cancer survival to measure progress in cancer control: A tutorial

Gap acceptance behaviour and crash risks of mobile phone distracted young drivers at roundabouts: A random parameters survival model

Dynamic survival analysis: Modelling the hazard function via ordinary differential equations

What can hospital emergency admissions prior to cancer diagnosis tell us about socio-economic inequalities in cancer diagnosis? Evidence from population-based data in England

Extended excess hazard models for spatially dependent survival data

Individual frailty excess hazard models in cancer epidemiology

Additive Bayesian Variable Selection under Censoring and Misspecification

On near‐redundancy and identifiability of parametric hazard regression models under censoring

Functional forms of socio-territorial inequities in breast cancer screening – A French cross-sectional study using hierarchical generalised additive models

Bayesian variable selection and survival modeling: assessing the Most important comorbidities that impact lung and colorectal cancer survival in Spain

MEGH: A parametric class of general hazard models for clustered survival data

A unifying framework for flexible excess hazard modelling with applications in cancer epidemiology

On a prior based on the Wasserstein information matrix

A tractable Bayesian joint model for longitudinal and survival data

Collaborators (5)

MIGUEL ANGEL LUQUE FERNANDEZ

Reza Drikvandi

Hein Putter

Giampiero Marra

Bernard Rachet

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