Mathematical Colloquium (at King's College London): A duality in the foundations of probability and statistics through history by Vladimir Vovk
| Personal Website | https://sites.google.com/site/fjavierrubio67/ |
| GitHub | https://github.com/FJRubio67 |
F. Javier Rubio
@FJavierRubio
- 148 Followers
- 150 Following
- 146 Posts
Lecturer at the Department of Statistical Science of UCL. All opinions my own. #rstats #JuliaLang #Bayesian #Statistics #Biostatistics 🇲🇽🇬🇧
New paper with J.A. Christen, just accepted in Statistical Methods in Medical Research
"Hazard-based distributional regression via ordinary differential equations"
preprint: http://arxiv.org/abs/2512.16336
R and Julia code + data: https://github.com/FJRubio67/SurvMODE
New paper, with P. Basak, A.R. Linero, and C. Maringe, accepted in JASA A&CS
"Understanding Inequalities in Cancer Survival Using Bayesian Machine Learning"
https://doi.org/10.1080/01621459.2025.2547968
#inequalities #cancer #Bayesian #MachineLearning
Most cancer patients face comorbidities that complicate survival. We use Bayesian machine learning (BART) in a relative survival framework to estimate excess hazard, uncover vulnerable subgroups, and identify drivers of inequalities in colon cancer survival.
Interesting short paper about the debates on Bayesian inference in the 1950s
Bayesian issues in the 1950s: an episode involving Karl Popper and Jimmie Savage
- Stephen M Stigler
My short paper with JA Christen
"On harmonic oscillator hazard functions"
has been accepted for publication in Statistics & Probability Letters.
https://doi.org/10.1016/j.spl.2024.110304
R #rstats code and data can be found at:
New preprint with P. Basak, A. Linero, and C. Maringe
"Relative Survival Analysis Using Bayesian Decision Tree Ensembles"
Relative Survival Analysis Using Bayesian Decision Tree Ensembles
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 to that of the general population. This framework assumes that an individual's hazard function is the sum of a known population hazard and an excess hazard associated with the cancer. Several estimands are derived from the excess hazard, including the \emph{net survival}, which are used to inform decisions and to assess the effectiveness of interventions on cancer management. In this paper, we introduce a Bayesian machine learning approach to estimating the excess hazard and identifying vulnerable subgroups, with a higher excess risk, using Bayesian additive regression trees (BART). We first develop a proportional hazards extension of the BART model to the relative survival setting, and then extend this model to non-proportional hazards. We develop tools for model interpretation and posterior summarization and then present an application using colon cancer data from England, highlighting the insights our proposed methodology offers when paired with state-of-the-art data linkage methods. This application demonstrates how these methods can be used to identify drivers of inequalities in cancer survival through variable importance quantification.
On Monday 16/12/2024, I will be presenting a joint work with Adam Iqbal and Emmanuel Ogundimu on Bayesian variable selection for (Heckman) sample selection models at #CMStatistics2024. There will be other talks on model selection, BART, and generalized Heckman models as well. Feel free to join us and say hi if you are around.
New #arxiv (short) preprint with J.A. Christen (@cimatoficial)
"On harmonic oscillator hazard functions"
http://arxiv.org/abs/2408.15964
R code and data are available at:
On harmonic oscillator hazard functions
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, facilitating likelihood and Bayesian inference on the parameters. We show that this model can capture a range of hazard shapes, such as increasing, decreasing, unimodal, bathtub, and oscillatory patterns, and characterize the tails of the corresponding survival function. We illustrate the use of this model in survival analysis using real data.
My paper with A. Christen (@cimatoficial):
"Dynamic survival analysis: modelling the hazard function via ordinary differential equations"
has been accepted for publication in Statistical Methods in Medical Research.
Dynamic survival analysis: modelling the hazard function via ordinary differential equations
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 the hazard function using systems of autonomous ordinary differential equations (ODEs). This modelling approach can be used to provide qualitative and quantitative analyses of the evolution of the hazard function over time. Our proposal capitalises on the extensive literature of ODEs which, in particular, allow for establishing basic rules or laws on the dynamics of the hazard function via the use of autonomous ODEs. We show how to implement the proposed modelling framework in cases where there is an analytic solution to the system of ODEs or where an ODE solver is required to obtain a numerical solution. We focus on the use of a Bayesian modelling approach, but the proposed methodology can also be coupled with maximum likelihood estimation. A simulation study is presented to illustrate the performance of these models and the interplay of sample size and censoring. Two case studies using real data are presented to illustrate the use of the proposed approach and to highlight the interpretability of the corresponding models. We conclude with a discussion on potential extensions of our work and strategies to include covariates into our framework. Although we focus on examples on Medical Statistics, the proposed framework is applicable in any context where the interest lies on estimating and interpreting the dynamics hazard function.
Our paper "Extended excess hazard models for spatially dependent survival data" has been accepted for publication in SMMR. The journal version is now available at:
