My current project is focused on the development of novel statistical tools for the analysis of epigentic data, namely DNA methylation. A key research problem within this area in the estimation of cell composition in individual mixed samples, for the purposes of adjustment in Epigenome-wide Association Studies (EWAS). My current research in this area has focussed on supervised and unsupervised approaches to address this problem. This work forms part of an active collaboration with the Institute for Health and Biomedical Innovation (IHBI) and the Wellcome Trust Centre for Human Genetics at the University of Oxford.
My previous research has been conducted in collaboration with the Cooperative Research Centre for Spatial Information http://www.crcsi.com.au . During this project, I developed spatial modelling approaches for the analysis of health services data, with a focus on the prediction of service-specific catchments and the representation of excess spatial variation in utilisation rates.
White, N. M., Mengersen, K. L. (2015) Predicting health program participation: a gravity-based, hierarchical modelling approach. Journal of the Royal Statistical Society: Series C (Applied Statistics), 65(1), 145-166.
Baker, J., White, N., & Mengersen, K. (2015). Spatial modelling of type II diabetes outcomes: a systematic review of approaches used. Royal Society Open Science, 2(6), 140460.
Cramb, S. M., Baade, P. D., White, N. M., Ryan, L. M., & Mengersen, K. L. (2015). Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis. Cancer epidemiology, 39(3), 430-439.
Wiemers, P., Marney, L., Muller, R., Brandon, M., Kuchu, P., Kuhlar, K., Uchime, C., Kang, D., White, N., Greenup, R., Fraser, J., Yadav, S., Tam, R. (2014) Cardiac Surgery in Indigenous Australians–How Wide is ‘The Gap’?. Heart, Lung and Circulation, 23(3), 265-272.
Wiemers, P., Marney, L., White, N., Hustig, A., Tan, W., Cheng, C. S., Kang, D., Yadav, S., Fraser, J., Tam, R. (2014) Midterm Results of Coronary Artery Bypass Grafting in an Australian Indigenous Population. Heart, Lung and Circulation23: e34-e35.
Baker, J., White, N., Mengersen, K. (2014). Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes. International journal of health geographics 13: 47.
White, N., Johnson, H., Silburn, P. Rousseau, J., Mengersen, K. (2013) Hidden Markov models for complex stochastic processes: A case study in electrophysiology. In Case Studies in Bayesian Statistical Modelling and Analysis (Eds. Alston, C., Mengersen, K. and Pettitt, A.) Wiley Series in Probability and Statistics.
Rolfe, M., White, N. and Chen, C. (2013) Latent class models in medicine. In Case Studies in Bayesian Statistical Modelling and Analysis (Eds. Alston, C., Mengersen, K. and Pettitt, A.) Wiley Series in Probability and Statistics.
Earnest, A., Cramb, S. White, N. (2013) Disease mapping using Bayesian hierarchical models. In Case Studies in Bayesian Statistical Modelling and Analysis (Eds. Alston, C., Mengersen, K. and Pettitt, A.) Wiley Series in Probability and Statistics.
White, N., Johnson, H., Silburn, P., Mellick, G., Dissanayaka, N., Mengersen, K. (2012) Probabilistic subgroup identification using Bayesian finite mixture modelling: A case study in Parkinson’s disease phenotype identification.Statistical Methods in Medical Research, 21: 563-583.
White, N., Johnson, H., Silburn, P., Mengersen, K. (2012) Dirichlet Process mixture models for unsupervised clustering of symptoms in Parkinson’s disease. Journal of Applied Statistics, 39: 2363-2377.
White, N., Benton, M., Lea R., Griffiths, L., Mengersen, K. Cellular heterogeneity in DNA methylation: A new approach for estimation of cellular proportions in whole blood. Invited talk at B3 2015: Big Biology and Bioinformatics.