Prof. James McGree

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Qualification

PhD, Statistics, University of Queensland, Australia. 2008

Research Interests

  • Design of experiments
  • Bayesian design
  • Clinical trial design

Selected research outputs (also see Google Scholar and www.jamesmcgree.com)

  • Overstall, A. M. and McGree, J. M. (2024) General Bayesian L2 calibration of mathematical models. Technometrics. Tentatively accepted for publication.
  • De Silva, D., Fisher, R., Radford, B., Thompson, H. and McGree, J. M. (2024) Model-robust Bayesian design through Generalised Additive Models for monitoring submerged shoals. Annals of Applied Statistics. Accepted for publication.
  • Buchhorn, K., Mengersen, K., Santos-Fernandez, E., Peterson, E., and McGree, J. M. (2024) Bayesian design with sampling windows for complex spatial processes. Journal of the Royal Statistical Society, Series C, 73, 378-397.
  • Mahendran, A., Thompson, H. and McGree, J. M. (2023) A model robust sub-sampling approach for generalised linear models in big data settings. Statistical Papers, 64, 1137-1157.
  • Senarathne, S. G. J., Mueller, W. and McGree, J. M. (2023) Bayesian design for minimising uncertainty in spatial processes. Biometrical Journal, 65, 2100386.
  • Santos-Fernandez, E., Ver Hoef, J. M., Peterson, E. E., McGree, J. M., Isaak, D. J. and Mengersen, K. (2023) SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks. R Journal, 15, 26-58.
  • McGree, J. M., Hockham, C., Kotwal, S., Wilcox, A., Bassi, A., Pollock, C., Burrell, L. M., Snelling, T., Jha, V., Jardine, M. and Jones, M. (2022) Controlled evaLuation of Angiotensin Receptor Blockers for COVID-19 respIraTorY disease (CLARITY): Statistical analysis plan for a randomised controlled Bayesian adaptive sample size trial. Trials, 23, 361.
  • Overstall, A. M. and McGree, J. M. (2022) Bayesian decision-theoretic design of experiments under an alternative model. Bayesian Analysis, 17, 1021-1041.
  • Santos-Fernandez, E., Ver Hoef, J. M., Peterson, E. E., McGree, J. M., Isaak, D. J. and Mengersen, K.(2022) Bayesian spatio-temporal models for stream networks. Computational Statistics & Data Analysis, 170, 107446.
  • Jardine, M., Kotwal, S., Bassi, A., Hockham, C., Jones, M., Wilcox, A., Pollock, C., Burrell, L. M., McGree, J. M., Rathore, V., Jenkins, C., Gupta, L., Ritchie, A., Bangi, A., D’Cruz, S., McLachlan, A., Finfer, S., Cummins, M., Snelling, T. and Jha, V. (2022) Angiotensin receptor blockers for the treatment of COVID-19: pragmatic, adaptive, multicentre, phase 3, randomised controlled trial. BMJ, 379, e072175.
  • Senarathne, S. G. J., Overstall, A. M. and McGree, J. M. (2020) Bayesian adaptive N-of-1 trials for estimating population and individual treatment effects.  Statistics in Medicine, 39, 4499-4518.
  • Overstall, A. M. and McGree, J. M. (2020) Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation. Bayesian Analysis, 15, 103-131.
  • Gedara, J., Drovandi, C. C. and McGree, J. M. (2020) A Laplace-based algorithm for Bayesian adaptive design. Statistics and Computing, 30, 1183-1208.
  • Cespedes, M. I., McGree, J. M., Drovandi, C. C., Mengersen, K., Fripp, J. and Doecke, J. (2020) Age dependent network dynamics via Bayesian hierarchical models reveal spatio-temporal patterns of neurodegeneration. Statistics in Medicine, 39, 2695-2713.
  • Firn, J., McGree, J. M., Harvey, E., Schütz, M., Flores, H., Buckley, Y. M., Lind, E. M., Borer, E. T., Seabloom, E. W., La Pierre, K. J., MacDougall, A. M., Prober, S. M., Stevens, C. J., Sullivan, L., Porter, E., LaDouceur, E., Allen, C., Moromizato, K. H., Eisenhauer, N., Wright, J., Arnillas, C. A., Harpole, W. S. and Risch, A.C. (2019) Leaf nutrient contents, but not specific leaf area, increase rapidly and predictably in response to eutrophication. Nature Ecology and Evolution, 3, 400-406.
  • Fisher, R., Shiell, G., Sadler, R., Inostroza, K., Shedrawi, G., Holmes, T. and McGree, J. M. (2019) ‘epower’: an R package for power analysis of Before-After-Control-Impact (BACI) designs. Methods in Ecology and Evolution, 10, 1843-1853.
  • Dehideniya, M., Drovandi, C. C. and McGree, J. M. (2018) Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology. Computational Statistics & Data Analysis, 124, 277-297.
  • Overstall, A. M., McGree, J. M. and Drovandi, C. C. (2017) An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions. Statistics and Computing, 28, 343-358.
  • Drovandi, C. C., Holmes, C., McGree, J. M., Mengersen, K., Ryan, E. and Richardson, S. (2017) Principles of experimental design for big data analysis. Statistical Science, 32, 385-404.
  • McGree, J. M. (2017) Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design. Computational Statistics & Data Analysis, 113, 207-225.
  • Woods, D. C., McGree, J. M. and Lewis, S. M. (2017) Model selection via Bayesian information capacity designs for generalised linear models. Computational Statistics & Data Analysis, 113, 226-238.
  • McGree, J. M., Drovandi, C. C., White, G. and Pettitt, A. N. (2016) A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design. Statistics and Computing, 26, 1121-1136.
  • Liu, S., McGree, J. M., Ge, Z. and Xie, Y. (2015). Computational and statistical methods for analysing big data with applications. Cambridge University Press.
  • Ryan, E., Drovandi, C. C., McGree, J. M. and Pettitt, A. N. (2016) A review of modern computational algorithms for Bayesian optimal design. International Statistical Review, 84, 128-154.
  • Drovandi, C. C., McGree, J. M. and Pettitt, A. N. (2014) A sequential Monte Carlo algorithm to incorporate model uncertainty in Bayesian sequential design. Journal of Computational and Graphical Statistics, 23, 3-24.
  • Drovandi, C. C., McGree, J. M. and Pettitt, A. N. (2013) Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data. Computational Statistics & Data Analysis, 57, 320-335.
  • McGree, J. M. and Eccleston, J. A. (2012) Robust designs for Poisson regression models. Technometrics, 54, 64-72.
  • McGree, J. M., Drovandi, C. C., Thompson, H. M., Eccleston, J. A., Duffull, S. B., Mengersen, K., Pettitt, A. N. and T. Goggin (2012) Adaptive Bayesian compound designs for dose finding studies. Journal of Statistical Planning and Inference, 142, 1480-1492.
  • Denman, N. G., McGree, J. M., Eccleston, J. A. and Duffull, S. B. (2011) Design of experiments for bivariate binary responses modelled by Copula functions. Computational Statistics & Data Analysis, 55, 1509-1520.

Selected PhD projects

  • Bayesian adaptive design for monitoring submerged shoals
  • Experimental design methods for efficient inference in large data settings
  • Adaptive design methods for large-scale stream networks
  • New statistical methods to design experiments for mechanistic models
  • Bayesian design for dependent data (Completed)
  • Optimal Bayesian experimental designs for complex models (Completed)
  • Model-based adaptive monitoring: Improving the effectiveness of reef monitoring programs (Completed)
  • Detection of longitudinal brain atrophy patterns consistent with progression towards Alzheimer’s Disease (Completed)
  • Modelling Parkinson Disease using Bayesian Variable Selection and Association Analysis (Completed)
  • Bayesian modelling of breast cancer data (Completed)
  • Resilience of South-East Queensland’s water supply (Completed)
  • Bayesian models for spatio-temporal assessment of disease (Completed)
  • Bayesian survival analysis using gene expression (Completed)
  • Bayesian algorithms with applications (Completed)
  • Optimal experimental design for nonlinear models with pharmacokinetic-pharmacodynamic applications (Completed)

Contact details

  • Location: GP-O514
  • Phone: (+61) 7 3138 2313
  • Email: james.mcgree@qut.edu.au