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PhD (University of Queensland), Bachelor of Science (University of Queensland)
Research interests:
- Optimal experimental design
- Bayesian (adaptive) design
- Innovative clinical trial design
Additional information
- Overstall, A. & McGree, J. (2022). Bayesian Decision-Theoretic Design of Experiments Under an Alternative Model. Bayesian Analysis, 17(4), 1021–1041. https://eprints.qut.edu.au/240029
- Senarathne, S., Drovandi, C. & McGree, J. (2020). A Laplace-based algorithm for Bayesian adaptive design. Statistics and Computing, 30(5), 1183–1208. https://eprints.qut.edu.au/131596
- Overstall, A. & McGree, J. (2020). Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation. Bayesian Analysis, 15(1), 103–131. https://eprints.qut.edu.au/150900
- Overstall, A., McGree, J. & Drovandi, C. (2018). An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions. Statistics and Computing, 28(2), 343–358. https://eprints.qut.edu.au/98452
- Dehideniya, D., Drovandi, C. & McGree, J. (2018). Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology. Computational Statistics and Data Analysis, 124, 277–297. https://eprints.qut.edu.au/97824
- McGree, J., (2017). Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design. Computational Statistics and Data Analysis, 113, 207–225. https://eprints.qut.edu.au/86673
- McGree, J., Drovandi, C., White, G. & Pettitt, T. (2016). A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design. Statistics and Computing, 26(5), 1121–1136. https://eprints.qut.edu.au/77732
- Ryan, E., Drovandi, C., McGree, J. & Pettitt, T. (2016). A review of modern computational algorithms for Bayesian optimal design. International Statistical Review, 84(1), 128–154. https://eprints.qut.edu.au/75000
- Drovandi, C., McGree, J. & Pettitt, T. (2014). A sequential Monte Carlo algorithm to incorporate model uncertainty in Bayesian sequential design. Journal of Computational and Graphical Statistics, 23(1), 3–24. https://eprints.qut.edu.au/49601
- McGree, J. & Eccleston, J. (2012). Robust designs for Poisson regression models. Technometrics, 54(1), 64–72. https://eprints.qut.edu.au/45857
- Title
- A precision medicine clinical trial platform to BEAT CF
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- 2014916
- Start year
- 2023
- Keywords
- cystic fibrosis; cystic fibrosis transmembrane regulator (CFTR); clinical trial; respiratory medicine; complex genetic disease
- Title
- Repurposing Existing Medications to Reduce Severe Acute Respiratory Distress in Patients with COVID-19: the CLARITY trial
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- 2002277
- Start year
- 2022
- Keywords
- respiratory viruses; renin-angiotensin system (RAS); acute respiratory distress syndrome (ARDS); angiotensin receptor; anti-hypertensive therapy
- Title
- Precision Ecology: The Modern Era of Designed Experiments in Plant Ecology
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DP200101263
- Start year
- 2020
- Keywords
- Title
- Managing Complex Networks in Endangered Grasslands to Restore Food Webs
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DP190100500
- Start year
- 2019
- Keywords
- Title
- Innovating Optimal Experimental Design through Bayesian Statistics
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DP120100269
- Start year
- 2012
- Keywords
- Sequential Monte Carlo; Bayesian Adaptive Design; Markov Chain Monte Carlo; Optimal Design; Bayesian Computation
- New methods in Bayesian design to monitor submerged shoals off the coast of Western Australia
PhD, Principal Supervisor - New Methods in Experimental Design for Subsampling Big Data
PhD, Principal Supervisor - Improving fruit supply chain operations through experimental design and predictive models
PhD, Principal Supervisor - Robust methods to design Bayesian adaptive clinical trials
PhD, Principal Supervisor
Other supervisors: Dr David Warne
- Bayesian Design for Sampling Anomalous Data on River Networks (2024)
- Bayesian System Identification for Nonlinear Dynamical Vehicle Models (2021)
- Model-Based Adaptive Monitoring: Improving the Effectiveness of Reef Monitoring Programs (2021)
- Experimental Design for Dependent Data (2020)
- Optimal Bayesian Experimental Designs for Complex Models (2019)
- Detection of Longitudinal Brain Atrophy Patterns Consistent with Progression Towards Alzheimer's Disease (2018)
- Statistical methods for modelling falls and symptoms progression in patients with early stages of Parkinson's disease (2018)
- Bayesian Statistical Models for Understanding Health-Related Outcomes for Women Screened for Breast Cancer (2016)
- Bayesian models for spatio-temporal assessment of disease (2014)
- Bayesian Algorithms with Applications (2012)