From its earliest beginnings, Bayesian statistics has been a synthesis of theory, methodology, computation and application. In this presentation, I will reflect on the development of the award-winning Australian Cancer Atlas (
https://atlas.cancer.org.au/) and spotlight its Bayesian foundations. I will highlight some of the challenges and proposed solutions to modelling and visualisation of an awkward spatial geography, “filling in” missing covariates, and communicating uncertainty. I will also touch on new research directions inspired by the Atlas: new spatio-temporal models, spatial vulnerability indices, meta-analysis transfer learning, distributed AI and responsible data science. Importantly, these methodological discussions will be complemented by reflections on the impact of the work for patients, health practitioners, cancer support groups and government agencies. Bayesian statistics really can make a difference!
Selected References
Baade P, K Mengersen [2024] Building HOPE through the Australian Cancer Atlas Insight+ MJA 35 //insightplus.mja.com.au/2024/35
J Bon, A Bretherton, K Buchhorn, S Cramb, C Drovandi, C Hassan, A Jenner, H Mayfield, J. McGree, K Mengersen, A Price, R Salomone, E Santos-Fernandez, J Vercelloni & X Wang, [2023] Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics. Philosophical Transactions. Series A, Mathematical, physical, and engineering sciences, 381(2247), Article number: 20220156.
Bretherton A, Bon J, Warne D, Mengersen K, Drovandi C, [2026] A Principled Approach to Bayesian Transfer Learning, Bayesian Analysis. To appear.
Cramb SM, K Mengersen, and PD Baade. [2011] Developing the atlas of cancer in Queensland: methodological issues. International Journal of Health Geographics, 10, p 1-11, 2011
Goodwin S, T Saunders, J Aitken, P Baade, U Chandrasiri, D Cook, S Cramb, E Duncan, S Kobakian, J. Roberts, K. Mengersen, [2024] Designing the Australian Cancer Atlas: visualizing geostatistical model uncertainty for multiple audiences. JAMIA. Journal of the American Medical Informatics Association, //doi.org/10.1093/jamia/ocae212
Hassan, C [2024] Structured Models and Algorithms for Sensitive Data. PhD Thesis, Queensland University of Technology, Australia.
Hogg J, J Cameron, S Cramb, P Baade, K Mengersen [2024] A Two‐stage Bayesian Small Area Estimation Approach for Proportions. International Statistical Review. V92 I3 455482.
Hyland-Wood B, Snoswell A, Sandeep R, Chun O, Perrin D, Fielt E, Price A, Mengersen K (2024) Response to proposals paper on introducing mandatory guardrails for AI in highrisk settings. Analysis and Policy Observatory, 2024/10/4
Jahan F, Duncan E, Cramb S, Baade P, Mengersen K, [2020] Multivariate Bayesian metaanalysis: Joint modelling of multiple cancer types using summary statistics, International Journal of Health Geographics, 19 (1)
Leontyeva Y, Y Huang, S Cramb, J Cameron, P Baade, K Mengersen, et al. [2025] Bayesian Spatial Relative Survival Model to Estimate the Loss in Life Expectancy and Crude Probability of Death for Cancer Patients, Statistics in Medicine 44 (3–4), e10287
Price A, M Rigby, P Fiévez, K Mengersen [2025] A spatial vulnerability index for environmental health Ecological Indicators Vol 178, September 2025, 113793
