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Doctor of Philosophy (Queensland University of Technology)
Dr White is a statistician with the Australian Centre for Health Services Innovation (AusHSI) and the QUT Centre for Healthcare Transformation. After obtaining her PhD in Statistics in 2011, Nicole's research has combined developments in statistical modelling with their application to understanding complex health data. Nicole is an experienced cross-disciplinary researcher who has collaborated with academics, clinicians, and decision-makers across a range of health and medical research areas, including critical care, hospital-acquired complications, and infectious diseases. Nicole's contributions to these areas have allowed her to develop expertise in analysing various data, including from clinical trials, linked hospital datasets and large-scale observational studies.
As a statistician with AusHSI, Nicole works closely with health economists and implementation scientists to evaluate the impacts of health service redesign and new models of patient care. She also leads the statistical analysis of large-scale research projects funded by the National Health and Medical Research Council and Medical Research Futures Fund.
Nicole has co-authored over 40 peer-reviewed publications and has been awarded more than $5 million in research funding as a chief investigator, including an Advance Queensland Industry Research Fellowship in 2020.
Dr White is an experienced supervisor of postgraduate students, with her students leading research that combines advanced statistical methods with their robust application to health data to improve decision making.
Outside of QUT, Nicole serves on the Biostatistics & Bioinformatics and Mentoring committees for the Statistical Society of Australia. In 2020, Nicole was named a Science and Technology Superstar of STEM. As part of this role, she is engaged in high school outreach to promote career pathways in mathematics and statistics.
Additional information
- White, N., Balasubramaniam, T., Nayak, R. & Barnett, A. (2022). An observational analysis of the trope 'A p-value of < 0.05 was considered statistically significant' and other cut-and-paste statistical methods. PLoS ONE, 17(3). https://eprints.qut.edu.au/228965
- White, N., Barnett, A., Hall, L., Mitchell, B., Farrington, A., Balcon, K., Paterson, D., Riley, T., Gardner, A., Page, K., Gericke, C. & Graves, N. (2020). Cost-effectiveness of an environmental cleaning bundle for reducing healthcare-associated infections. Clinical Infectious Diseases, 70(12), 2461–2468. https://eprints.qut.edu.au/132396
- Mitchell, B., Hall, L., White, N., Barnett, A., Halton, K., Paterson, D., Riley, T., Gardner, A., Page, K., Farrington, A., Gericke, C. & Graves, N. (2019). An environmental cleaning bundle and health-care-associated infections in hospitals (REACH): a multicentre, randomised trial. The Lancet Infectious Diseases, 19(4), 410–418. https://eprints.qut.edu.au/197640
- Wolkewitz, M., Lambert, J., von Cube, M., Bugiera, L., Grodd, M., Hazard, D., White, N., Barnett, A. & Kaier, K. (2020). Statistical analysis of clinical COVID-19 data: A concise overview of lessons learned, common errors and how to avoid them. Clinical Epidemiology, 12, 925–928. https://eprints.qut.edu.au/205313
- White, N., Benton, M., Kennedy, D., Fox, A., Griffiths, L., Lea, R. & Mengersen, K. (2017). Accounting for cell lineage and sex effects in the identification of cell-specific DNA methylation using a Bayesian model selection algorithm. PLoS One, 12(9), 1–18. https://eprints.qut.edu.au/112167
- Hydrogel Spacers in External Beam Radiation Therapy of Prostate Cancer: Patient Selection and Cost-Effectiveness (2020)
- Statistical Inference for the Investigation of Cell-Type Heterogeneity in DNA Methylation Data (2019)
- Ensemble Statistical Modelling of Risk Factors in Health (2018)
- Statistical methods for modelling falls and symptoms progression in patients with early stages of Parkinson's disease (2018)
- Bayesian Approaches to Issues Arising in Spatial Modelling (2017)
- Bayesian Spatiotemporal Modelling of Chronic Disease Outcomes (2017)
- Bayesian Estimation of the Number of Components in Mixture and Hidden Markov Models: Methods and Applications (2016)