PhD (Statistics), QUT.
B AppSc (Hons: Statistics), QUT.
B Math (Statistics), QUT.
B Bus (Accounting, International Business), QUT.
- Spatial modelling (e.g. disease mapping)
- Spatio-temporal modelling
- Data visualisation (especially spatial visualisations, e.g. choropleth and other thematic maps, cartograms)
- Bayesian mixture models and development of relabelling algorithms to reverse label switching
- Environmental statistics and sustainability
Recent and Current Projects
My most recent project was the development of the Australian Cancer Atlas. The output is a publicly accessible , online, interactive map (think Google Earth) showing estimated incidence and relative survival rates for many different types of cancers across Australia at the small area level (SA2s). This was joint work with the Cancer Council Queensland and the QUT Visualisation and eResearch (ViseR) team to combine our expertise in statistical spatial modelling, data visualisation, and UX and UI design. A screen-capture of the atlas is shown below.
For more information about the Australian Cancer Atlas, please visit the website: http://atlas.cancer.org.au/.
There are many extensions planned for the atlas, including:
- Spatio-temporal modelling to see how patterns of a given cancer change over time.
- Investigating different measures of survival (e.g. loss of life expectancy).
- Ecological modelling (adding additional covariates based on area-specific socio-demographics or clinical characteristics).
- Exploring relationships/clusters between cancers.
- Adding new data sources including estimates for small-area variation in cancer screening.
There are also plans to develop a new spatial model and investigate the issue of over- and under-smoothing. Earl is currently working on these research areas.
Duncan, E. W. 2017. Bayesian approaches to issues arising in spatial modelling. PhD Thesis, Queensland University of Technology. URL: https://eprints.qut.edu.au/112356/1/Earl_Duncan_Thesis.pdf.
Published (most recent first):
- Mengersen, K., E. Duncan, J. Arbel, C. Alston-Knox, and N. White. 2018. “Chapter 15: Applications in Industry”. In Handbook of Mixture Analysis, Handbooks of Modern Statistical Methods Series, edited by S. Früwirth-Schnatter, G. Celeux, and C. P. Robert. Milton: Chapman & Hall/CRC.
- Cramb, S. M., E. W. Duncan, K. L. Mengersen, and P. D. Baade. 2018. Australian Cancer Atlas, small-area incidence: technical report. Brisbane: Cancer Council Queensland and Queensland University of Technology (QUT). (Not publicly available).
- Cramb, S. M., E. W. Duncan, K. L. Mengersen, and P. D. Baade. 2018. Australian Cancer Atlas, small-area survival: technical report. Brisbane: Cancer Council Queensland and Queensland University of Technology (QUT). (Not publicly available).
- Cramb, S. M., E. W. Duncan, P. D. Baade, and K. L. Mengersen. 2018. Investigation of Bayesian spatial models. Brisbane: Cancer Council Queensland and Queensland University of Technology (QUT). URL: https://eprints.qut.edu.au/115590.
- Duncan, E. W., N. M. White, and K. Mengersen. 2017. Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference. International Journal of Health Geographics 16 (1): 47. DOI: 10.1186/s12942-017-0120-x.
- Cramb, S. M., E. W. Duncan, N. M. White, P. D. Baade, and K. L. Mengersen. 2016. Spatial Modelling Methods. Brisbane: Cancer Council Queensland and Queensland University of Technology (QUT).
- Duncan, E. W., N. M. White, and K. Mengersen. 2016. Bayesian spatiotemporal modelling for identifying unusual and unstable trends in mammography utilisation. BMJ Open 6 (5): p.e010253. DOI: 10.1136/bmjopen-2015-010253.
- Pokorny, M. R., M. de Rooij, E. Duncan, F. H. Schröder, R. Parkinson, J. O. Barentsz, and L. C. Thompson. 2014. Prospective Study of Diagnostic Accuracy Comparing Prostate Cancer Detection by Transrectal Ultrasound–Guided Biopsy Versus Magnetic Resonance (MR) Imaging with Subsequent MR-guided Biopsy in Men Without Previous Prostate Biopsies. European Urology 66 (1): 22-29. DOI: 10.1016/j.eururo.2014.03.002.
Under review/ in progress:
- Mengersen, K., E. Duncan, S. Cramb et al. “A comparison of Bayesian spatial models for cancer incidence: theory and performance”. In TBA, edited by K. Mengersen, TBA. Forthcoming 2019/2020.
|July 2018||Risk Patterns of Lung Cancer Mortality in Northern Thailand||BMC Public Health||Published|
|Sep 2018||Evaluation of Bayesian Multiple Stage Estimation under Spatial CAR Model Variants||Journal of Statistical Computation and Simulation||Published|
|Jan 2019||A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix||Entropy||Published|
|Mar 2019||Spatial pattern consistency among different remote-sensing land cover||International Journal of Geo-Information||Under Review|
- 18 Aug 2016: Deriving the Full Conditionals
- 20 July 2017: Reversing Label Switching
- 29 March 2018: Cartograms
- 17 Jan 2019: Attempts to Quantify Under- and Over-smoothing