Dr Susanna Cramb
Associate Investigator
Susanna is a Strategic Research Fellow at the Institute of Health and Biomedical Innovation, QUT. She is a biostatistician skilled in using complex modelling techniques to investigate the spatial and spatio-temporal patterns of diseases, risk factors and outcomes in Queensland and across Australia. Susanna has worked in epidemiological research for over a decade, and her publications have been cited over 2700 times. Susanna’s research has also been used by Queensland Health in setting their cancer care objectives.
Susanna previously worked at Cancer Council Queensland as a Spatial Modeller and also at the School of Population Health, University of Queensland, where she was involved with large epidemiological projects, including the Australian Burden of Disease study, among others.
Research Interests:
Bayesian hierarchical models
Cancer epidemiology
Spatial statistics
Survival models
Qualifications:
B App Sc (Med Sc), QUT 2001
MPH&TM, JCU 2004
Grad Cert Sc (Stat), UQ 2007
PhD, QUT 2015
Projects
Publications
Books
Invited talks, refereed proceedings and other conference outputs
(2020). Bayesian multivariate analysis of disease, with application to the Australian Cancer Atlas.
2020 Joint Statistical Meetings.
(2019). Mapping cancer outcomes to identify and address inequities: the Australian Cancer Atlas.
Data Science and Social Good Symposium.
(2019). Evaluating the interplay between clusters, climatic covariates and spatial priors in spatio-temporal modelling of dengue in Makassar, Indonesia.
Bayes on the Beach 2019.
(2019). The Australian Cancer Atlas.
Locate19.
(2019). Multivariate Bayesian meta-analysis model to analyse estimated cancer incidence.
ACEMS Students and ECR Retreat.
(2019). Computing the Australian Cancer Atlas: getting it ‘just right’.
The 12th International Conference on Monte Carlo Methods and Applications.
(2019). Augmeting Disease Maps: a Bayesian meta-analysis approach.
Young Statisticians Conference 2019.
(2019). Small-area melanoma incidence patterns across Australia: the thick and thin of it.
Australasian Epidemiological Association Annual Scientific Meeting 2019.
(2019). Temporal Modeling of Dengue Fever: A Comprehensive Literature Review.
3rd International Conference on Mathematics, Sciences, Technology, Education and their application (ICSMTEA) in Conjunction with 1st International Symposium on Green Materials & Technology (ISGMT).. 967, 15-21. doi: 10.4028/www.scientific.net/MSF.967.15
(2019). Bayesian spatio-temporal conditional autoregressive localised model.
ACEMS Retreat 2019.
(2018). Making More of Spatial Maps: A Bayesian meta-analysis approach.
Workshop on Young Bayesians and Big Data for Social Good.
(2018). Bayesian disease mapping in R.
UseR! 2018.
(2018). Temporal modelling of dengue fever: A comprehensive literature review.
3rd International Conference on Mathematics, Sciences, Technology, Education and their application (ICSMTEA) in Conjunction with 1st International Symposium on Green Materials & Technology (ISGMT).
(2018). In the pursuit of research funding.
Joint International Society for Clinical Biostatistics and Australian Statistical Conference 2018. Early Career Researchers Day,
(2018). The impact of covariates on the grouping structure of a Bayesian spatio-temporal localised model.
Australasian Applied Statistics Conference 2018.
(2018). Making More of Spatial Maps: A Bayesian Meta-analysis approach.
ACEMS Students Retreat.
(2018). Bayesian spatio-temporal models using R-INLA.
ACEMS Retreat 2018.
(2018). Comparison of different Bayesian spatio-temporal models using R packages.
UseR! .
(2018). Comparing Spatio-Temporal models using CARBayes: An application to dengue fever in Makassar, Indonesia.
The joint international society for clinical Biostatistics and Australian Statistical Conference 2018.
(2018). The Australian Cancer Atlas: Models and Messages.
Joint International Society for Clinical Biostatistics and Australian Statistical Conference 2018.
(2017). Evaluating the impact of small number of areas on spatial estimation.
The Young Statistician Conference (YSC).
(2017). A new approach to small area cancer survival estimation.
Geomed 2017.
(2017). Bayesian spatial estimation when areas are few.
The international Biometric Society Australasian Region conference.
(2016). Head and neck cancers (HNC) among Indigenous Australians living in Queensland, 1997-2012.
World Indigenous Cancer Conference.
(2016). The great divide: Remoteness of residence and cancer survival differentials for Indigenous persons in Queensland.
World Indigenous Cancer Conference.
Journal Articles
(2020). Evaluating the impact of a small number of areas on spatial estimation.
International Journal of Health Geographics . 19(1), 39. doi: 10.1186/s12942-020-00233-1
(2020). Multivariate Bayesian meta-analysis: joint modelling of multiple cancer types using summary statistics.
International Journal of Health Geographics. 19(1), 42. doi: 10.1186/s12942-020-00234-0
(2020). Augmenting disease maps: a Bayesian meta-analysis approach.
Royal Society Open Science. 7(8), 192151. doi: 10.1098/rsos.192151
(2020). Climate variability and dengue fever in Makassar, Indonesia: Bayesian spatio-temporal modelling.
Spatial and Spatio-temporal Epidemiology. 33, 100335. doi: 10.1016/j.sste.2020.100335
(2020). Bayesian spatial survival models for hospitalisation of dengue: A case study of Wahidin hospital in Makassar, Indonesia.
International Journal of Environmental Research and Public Health. 17(3), 878. doi: 10.3390/ijerph17030878
(2019). Temporal trends in net and crude probability of death from cancer and other causes in the Australian population, 1984–2013.
Cancer Epidemiology. 62, 101568. doi: 10.1016/j.canep.2019.101568
(2019). Individual- and Area-Level Socioeconomic Inequalities in Esophageal Cancer Survival in Shandong Province, China: A Multilevel Analysis.
Cancer Epidemiology Biomarkers & Prevention. 28(9), 1427 - 1434. doi: 10.1158/1055-9965.EPI-19-0203
(2019). Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review.
Epidemiology & Infection. 147, e33. doi: 10.1017/S0950268818002807
(2019). Spatial variation in cervical cancer screening participation and outcomes among Indigenous and non-Indigenous Australians in Queensland.
Geographical Research. 57(1), 111-122. doi: 10.1111/1745-5871.12281
(2019). Development of the Australian Cancer Atlas: spatial modelling, visualisation, and reporting of estimates.
International Journal of Health Geographics. 18(1), 21. doi: 10.1186/s12942-019-0185-9
(2019). Area socioeconomic status is independently associated with esophageal cancer mortality in Shandong, China.
Scientific Reports. 9(1), 6388. doi: 10.1038/s41598-019-42774-x
(2017). Spatial variation in cancer incidence and survival over time across Queensland, Australia.
Spatial and Spatio-temporal Epidemiology. 23, 59 - 67. doi: 10.1016/j.sste.2017.09.002
(2017). A geostatistical model for combined analysis of point-level and area-level data using INLA and SPDE.
Spatial Statistics. 21, 27 - 41. doi: 10.1016/j.spasta.2017.04.006
(2016). Spatially varying coefficient inequalities: Evaluating how the impact of patient characteristics on breast cancer survival varies by location.
PLOS ONE. 11(5), doi: 10.1371/journal.pone.0155086
(2016). A flexible parametric approach to examining spatial variation in relative survival.
Statistics in Medicine. 35(29), 5448-5463. doi: 10.1002/sim.7071
(2016). Estimating cancer survival - improving accuracy and relevance.
Australian and New Zealand Journal of Public Health. 40(5), 403-404. doi: 10.1111/1753-6405.12610
(2016). Quantifying the changes in survival inequality for Indigenous people diagnosed with cancer in Queensland, Australia.
Cancer Epidemiology. 43, 1-8. doi: 10.1016/j.canep.2016.05.002
(2016). Spatio-temporal relative survival of breast and colorectal cancer in Queensland, Australia 2001–2011.
Spatial and Spatio-temporal Epidemiology. 19, 103-114. doi: 10.1016/j.sste.2016.08.002
(2016). Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data.
Geospatial Health. 11(2), doi: 10.4081/gh.2016.428
(2016). The international epidemiology of lung cancer: Latest trends, disparities, and tumor characteristics.
Journal of Thoracic Oncology. 11(10), 1653-1671. doi: 10.1016/j.jtho.2016.05.021
(2015). Does geographic location impact the survival differential between screen- and interval-detected breast cancers?.
Stochastic Environmental Research and Risk Assessment. 30(1), 155-165. doi: 10.1007/s00477-015-1050-4
(2015). Geographic variation in the intended choice of adjuvant treatments for women diagnosed with screen-detected breast cancer in Queensland.
BMC Public Health. 15(1), doi: 10.1186/s12889-015-2527-2
(2015). Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis.
Cancer Epidemiology. 39(3), 430-439. doi: 10.1016/j.canep.2015.03.001
Technical reports and unrefereed outputs
(2018). Regional variation in cervical cancer screening participation & outcomes among Indigenous and non-Indigenous Australians: Queensland (2006-2011).
(2018). Australian Cancer Atlas: small-area incidence technical report.
(2016). Communicating Statistical Outputs Through Health Maps.
(2016). Spatial Modelling Methods.
(2016). Grey Literature Review: Internet Published Cancer Maps.
