Dr Jeremy Oakley
Contents
Highlights
The SHeffield ELicitation Framework (SHELF)
SHELF is a package of documents, templates and software to carry out elicitation of probability distributions for uncertain quantities from a group of experts, developed by Tony O’Hagan and myself, and is available for download free of charge.
Managing Uncertainty in
Complex Models (MUCM) and the MUCM
Toolkit
The MUCM project is developing a technology that is capable of addressing all sources of uncertainty in model predictions and to quantify their implications efficiently, even in the most complex models. It has the potential to revolutionise scientific debate by resolving the contradictions in competing models. It will also have a radical effect on everyday modelling and model usage by making the uncertainties in model outputs transparent to modellers and end users alike. Online resources are available at the MUCM toolkit.
Biographical Information
I
am a lecturer in the Department of
Probability and Statistics at the University of Sheffield. I obtained my BSc
(1996) in Mathematics and Statistics from the University of Nottingham, and my
PhD (2000) in Statistics from the University of Sheffield. I have worked as a
postdoctoral research associate in both the Department
of Computer Science and Department of Probability and Statistics,
Research Interests
My research interests are in Bayesian inference, and in particular...
Statistical analysis of computer code outputs
Complex computer models are in widespread use to simulate complex
real-world processes. There is great concern about the accuracy of such models,
and how to quantify the various uncertainties in their use. Innovative Bayesian
methods are providing powerful tools to answer these and other important
questions facing users of such models.
Prior elicitation
Elicitation is the process of extracting expert knowledge about some
unknown quantity of interest, or the probability of some future event, which
can then be used to supplement any numerical data that we may have. If the
expert in question does not have a statistical background, as is often the
case, translating their beliefs into a statistical form suitable for use in our
analyses can be a challenging task.
Health economics
The main concern of the field of health economics is to examine the
cost-effectiveness of medical technologies. The Department of Probability and
Statistics, in collaboration with Sheffield's School of
Health and Related Research (ScHARR), has established the Centre for Bayesian Statistics in Health Economics.
For more information please visit the department's Bayesian Research Cluster web pages.
Papers
Available for download
Oakley, J.E., Brennan, A., Tappenden, P. and Chilcott,
J.B. (2009). Sample sizes
for Monte Carlo partial EVPI calculations. Research Report No. 568/06. Department of Probability and Statistics. [REVISED] Download example R
code.
Published papers
Stevenson, M. D., Oakley, J. E., Lloyd Jones, M. Brennan, A., Compston, J. E. , McCloskey E.
V. and Selby P. L. (2009). The Cost-Effectiveness of an RCT to Establish
Whether 5 or 10 Years of Bisphosphonate Treatment Is the Better Duration for
Women With a Prior Fracture. To appear in Medical Decision Making.
Nixon, R.M., O'Hagan, A., Oakley, J. E., Madan, J., Stevens, J.W.
Bansback, N. and Brennan, A. (2009) The Rheumatoid Arthritis Drug Development
Model: A case study in Bayesian clinical trial simulation. To appear in Pharmaceutical
Statistics.
Conti, S., Gosling, J. P., Oakley, J. E. and O'Hagan,
A. (2009). Gaussian process emulation of dynamic computer codes. Biometrika
96, 663-676.
Oakley, J. E. (2009). Decision-theoretic sensitivity analysis for complex
computer models. Technometrics 51, 121-129.
Stevenson, M. D., Oakley, J. E., Chick, S. E. and
Chalkidou, K. (2009). The cost-effectiveness of surgical instrument
management policies to reduce the risk of vCJD transmission to humans. Journal
of the Operational Research Society 60, 506-518.
Coyle D. and Oakley J. (2008) Estimating the expected value of partial
perfect information: a review of methods. The Eur. Journal of Health
Economics 9, 251-259.
Karnon J., McIntosh A., Coster J.,
Gosling,
J.P., Oakley, J.E. and O'Hagan, A. (2007). Nonparametric elicitation for heavy-tailed
prior distributions. Bayesian
Analysis, 2, 693-718.
Oakley, J. and O'Hagan, A. (2007). Uncertainty
in prior elicitations: a non-parametric approach. Biometrika 94,
427-441.
Coyle, D. and Oakley J. E. (2007). Assessing
the value of information in economic analysis: a comparison of methods. The
European Journal of Health Economics.
Karnon, J., McIntosh, A.,
O' Hagan, A., Buck, C. E.,
Daneshkhah, A., Eiser, J. E., Garthwaite, P. H., Jenkinson, D. J., Oakley, J.
E. and Rakow, T. (2006). Uncertain Judgements: Eliciting Expert
Probabilities. Chichester: Wiley.
Stevenson, M. D., Lloyd Jones, M., De Nigris E., Brewer,
N., Davis, S. and Oakley, J. (2005). A systematic review and economic
evaluation of alendronate, etidronate, risedronate, raloxifene and teriparatide
for the prevention and treatment of postmenopausal osteoporosis. Health
Technology Assessment, Vol.9: No. 22.
Stevenson, M. D., Brazier, J. E., Calvert, N.W.,
Lloyd-Jones M., Oakley, J. and Kanis, J.A. (2005). Description of an
individual patient methodology for calculating the cost-effectiveness of
treatments for osteoporosis in women. Journal of the Operational Research
Society, 56, 214-221.
Oakley, J. (2004). Estimating percentiles of computer code outputs. Applied
Statistics, 53, 83-93.
O'Hagan, A. and Oakley, J. E. (2004).
Probability is perfect, but we can’t elicit it perfectly. Reliability
Engineering and System Safety, 85, 239-248.
Oakley, J. and
O'Hagan, A. (2004). Probabilistic sensitivity analysis of complex
models: a Bayesian approach. Journal of the Royal Statistical Society Series
B, 66, 751-769. Download example data.
Stevenson M.D., Oakley, J. and Chilcott, J.B. (2004).
Gaussian process modelling in conjunction with individual patient simulation
modelling: A case study describing the calculation of cost-effectiveness ratios
for the treatment of osteoporosis. Medical Decision Making, 24(1),
89-100.
Tappenden, P., Chilcott, J. B., Eggington, S., Oakley, J. and McCabe, C.
(2004). Methods for expected value of information analysis in complex health
economic models: developments on the health economics of beta-inteferon and
glatiramer acetate for multiple sclerosis. Health Technology Assessment,
Vol. 8: No. 27.
Oakley, J. and O'Hagan, A. (2002). Bayesian
inference for the uncertainty distribution of computer model outputs. Biometrika,
89, 769-784.
Oakley, J. (2002). Eliciting Gaussian process priors for complex
computer codes. The Statistician, 51, 81-97.
O'Hagan, A., Kennedy. M. C. and Oakley, J. E. (1999). Uncertainty
analysis and other inference tools for complex computer codes (with
discussion). In Bayesian Statistics 6, J. M. Bernardo et al (eds.). Oxford
University Press, 503-524.
Contact Details
Jeremy Oakley
The
Department of Probability and Statistics
The
Hounsfield Road
Phone: +44-(0)114-2223853
Fax: +44-(0)114-2223809