Basak, Piyali; Maringe, Camille; Rubio, F Javier; Linero, Antonio R; (2025) Understanding Inequalities in Cancer Survival Using Bayesian Machine Learning. Journal of the American Statistical Association 10.1080/01621459.2025.2547968. (In press).

Text Understanding Inequalities in Cancer Survival Using Bayesian Machine Learning (1).pdf - Accepted Version Access restricted to UCL open access staff until 21 August 2026. Download (1MB)

Abstract

Most cancer patients are diagnosed after the age of 60, often with existing chronic health conditions (comorbidities), that can delay diagnosis and complicate treatment, prognosis, and monitoring. These comorbidities may exacerbate existing sociodemographic inequalities in cancer survival. While much research has focused on how comorbidities affect overall survival, national and international institutions typically prefer the relative survival framework for population-based studies. This framework decomposes an individual’s overall hazard into a known population hazard and an excess hazard attributable to cancer. Estimands derived from the excess hazard, such as net survival, are widely used to assess interventions and inform policy. In this paper, we use a Bayesian machine learning approach to estimate the excess hazard and identify vulnerable subgroups with a higher excess hazard, using Bayesian additive regression trees (BART). We develop a proportional hazards version of BART for the relative survival context and extend it to accommodate non-proportional hazards. We also provide tools for model interpretation and posterior summarization. This is applied to colon cancer data from England to provide insights when paired with state-of-the-art data linkage methods. We then identify drivers of inequalities in cancer survival through variable importance quantification.

Download activity - last month

Export as CSVXMLJSON

Download activity - last 12 months

Export as JSONXMLCSV

Downloads by country - last 12 months

Export as JSONXMLCSV

Archive Staff Only

View Item